CN111344591A

CN111344591A - Radar-based system and method for detecting objects and generating maps containing radial velocity data, and system for detecting and classifying Unmanned Aerial Vehicle (UAV)

Info

Publication number: CN111344591A
Application number: CN201880073307.0A
Authority: CN
Inventors: 沃特·谢耶尔; 赫尔本·帕克尔特
Original assignee: Robin Radar Facilities BV
Current assignee: Robin Radar Facilities BV
Priority date: 2017-11-13
Filing date: 2018-11-02
Publication date: 2020-06-26
Anticipated expiration: 2038-11-02
Also published as: US11428798B2; WO2019091867A1; EP3710852A1; CN111344591B; US20220413121A1; US20200326418A1

Abstract

A frequency modulated continuous wave, FMCW, radar system is provided. The radar system includes: one or more antennas configured to transmit and receive FMCW radar wave signals to scan a full circle to detect objects within coverage, and processing circuitry configured to provide scan data based on the transmitted and received FMCW radar signals and an azimuthal orientation of the antennas. In a first aspect, the processing circuitry is configured to generate first type radar maps, wherein each first type radar map contains range, radial velocity and return energy data for one or more detected objects, and the circuitry is further configured to generate second type radar maps, wherein each second type radar contains azimuth, range and return energy data for one or more detected objects. In the first aspect, the processing circuitry is further configured to generate a complete data type radar map by combining the first and second type radar maps with corresponding range data, whereby each complete data type radar map contains azimuth, range, radial velocity and echo energy data for one or more detected objects. In a second aspect, the processing circuitry is configured to generate a complete data-type radar map based on the obtained scan data, and the processing circuitry is further configured to select an unconventional type radar map from the complete data-type radar map, wherein the unconventional type radar map has velocity data representing positive and negative radial velocities within the observed range of radial velocities. The processing circuit may be further configured to generate object trajectories or Unmanned Aerial Vehicle (UAV) trajectories, wherein each object/UAV trajectory is based on at least two unconventional types of radar maps. In a third aspect, then, for the generated map of the object/UAV trajectory, the processing circuitry is configured to determine an outer energy sum of echo energies of range cells representing positive and negative radial velocity signals within the first and second outer radial velocity ranges, and to determine a central energy sum of echo energies of range cells representing radial velocity signals within a central range of the observed radial velocity range, and/or a total energy sum of echo energies of range cells representing all of the observed radial velocity signals. In a fourth aspect, there is provided an unmanned aerial vehicle, UAV, system comprising: a control station for controlling a first cooperative Unmanned Aerial Vehicle (UAV); and a radar system configured to scan for objects within the detection coverage and to provide scan data indicative of the objects detected within the coverage. The first UAV may be provided with a transponder containing identification information ID, and the first UAV and the control station may be configured for exchanging transponder data.

Description

Radar-based system and method for detecting objects and generating maps containing radial velocity data, and system for detecting and classifying Unmanned Aerial Vehicle (UAV)

Technical Field

The present disclosure relates to radar-based systems and methods for scanning and detecting objects using Frequency Modulated Continuous Wave (FMCW) radar systems, and more particularly to generating maps containing radial velocity data. The generated map may be used to track and classify objects including detected objects such as Unmanned Aerial Vehicles (UAVs).

The present disclosure also relates to a system for detecting and classifying an Unmanned Aerial Vehicle (UAV). Classification of UAVs may be used to distinguish between several detected UAVs.

Background

In recent years, the number of small Unmanned Aerial Vehicles (UAVs) available for civilian use has increased dramatically. These platforms may be used privately for leisure and movie shooting, but also for applications such as agriculture and environmental monitoring, surveillance, and disaster response. However, small UAVs may also be abused to conduct antisocial, unsafe, and even criminal activities, such as infringing privacy, danger of collision (with others, other UAVs, and larger aircraft), and even transportation of illegal substances. Accordingly, there is an increasing interest in developing sensor systems that can detect and track UAVs. Radar detection and tracking UAVs present significant challenges because small UAVs typically have lower radar scattering cross-sections and fly at lower speeds and altitudes as compared to conventional aircraft. Small UAVs are capable of highly variable motions, which complicates the task of separating them from a cluttered stationary background. The high mobility of small UAVs also makes the tracking problem more difficult because it is not possible to make strong assumptions about the expected UAV motions.

FMCW radar systems are well known and widely used in the automotive industry and other industrial applications, where they provide range and doppler information of a detected object or target, where the doppler shift can be converted to the radial velocity of the detected object or target.

During operation of the FMCW radar, the system transmits continuous radio energy at a frequency modulated by a triangular or sawtooth signal. Thus, the frequency of the transmitted signal varies gradually over time. When the signal is reflected by an object, the received waveform will create a delayed copy of the transmitted waveform, with the time delay as a measure of the target range. If the target is moving, the radar system will record the Doppler shift in the received signal. The received signal will show a higher frequency when the target is close compared to the frequency of the emitted signal, and a lower frequency when the target is moving away from the radar location. Thus, the total doppler shift may result from the superposition of source motion and observer motion. Specifically, the amount of doppler frequency shift is proportional to the radial velocity of the target.

In range-doppler processing, the range and velocity information of a moving object is compensated by applying a double fourier transform to the received signal. A first transform (range FFT) is applied to the signal received from the transmit up-scan to produce high-resolution range lines. The range FFT is repeated for a selected integration time to obtain an appropriate number of range lines, and then a second fourier transform (doppler FFT) is applied to the obtained plurality of range lines. The result is a so-called range-doppler plot or range-radial velocity plot, where one axis represents range and the other axis represents radial velocity. The range-doppler plot is a matrix of range-velocity cells containing echo signal values of the hit target, where the amplitude values of the range-velocity cells represent the echo energy of the transmitted radar wave signal transmitted from the target having a range and radial velocity indicated by the position of the range-velocity cell.

The range-doppler plot or matrix may be arranged with a plurality of individual target ranges along the x-axis and a plurality of individual velocity ranges along the y-axis, whereby the columns of the matrix represent the velocity detection span for a given individual target range and the rows represent the distance detection span for a given individual target velocity range. For a given single target range, several different target velocities with different echo signal values may be observed, wherein the observed radial velocity and echo signal values are represented by data of a velocity column corresponding to the single target range. The velocity column may be referred to as the so-called doppler signature (signature), while the range-doppler plot/matrix consists of the doppler signatures for all individual target ranges. For a given target range, the corresponding doppler signature data varies with time, and when successive doppler signatures for the same target range are obtained, such as by generating multiple successive range-doppler plots/matrices, these doppler signatures can be combined into a so-called doppler spectrogram to show the observed doppler frequency or radial velocity versus time for a single target range.

The high range resolution FMCW radar system is also used to detect and characterize small UAVs by using micro-doppler analysis on the obtained range-doppler data. The relative movement of the components is characteristic of different classes of targets, such as flapping of birds and rotation of propeller blades. In a range-doppler or range-radial velocity diagram, the moving parts of the body cause a characteristic doppler signature, in which the main contribution comes from the torso (torso) of the body, causing the doppler frequency of the target, while the flapping action of the bird wings or propeller blades cause modulation of the echo radar signal and produce sidebands around the central doppler frequency of the doppler signature, which may be referred to as a micro-doppler signature. Thus, the sideband width of the micro-doppler signature within the range-doppler plot/matrix may indicate the type of target hit by the transmitted radar wave. When using the radial velocity for the range-doppler plot, the sideband width of the micro-doppler signature will be given by the width of the radial velocity span of the micro-doppler signature.

In order to generate a trajectory of the target, a plurality of matching maps must be generated, wherein the maps contain range, azimuth, amplitude and radial velocity information of the verified target. Furthermore, FMCW radar systems may have to scan an object over a full circumferential range of 360 ° at a high repetition frequency, which requires a large number of rather complex signal processing steps, in particular in order to generate radial velocity information, to perform with limited processing power in a limited time.

Today's FMCW radar systems include a signal processor that clutter filters the returned radar wave signal and computes a range-doppler plot to obtain range, amplitude and radial velocity information for the verified target, which is then combined with the azimuth information, where the combined information is processed by a mapping processor to obtain a plot of the verified target. Since the FMCW radar system scans an object over a full circumferential range of 360 °, the scan data can be obtained with reference to a so-called radar image, which is divided into a plurality of image lines, which in turn are divided into a plurality of range cells, wherein each image line covers a given azimuth angle range and the total number of image lines covers the full circumferential azimuth angle range from 0 ° to 360 °.

For each image line containing a specific number of range cells and covering a specific azimuth range, a series of signals is transmitted within a time frame of the radar system covering the azimuth range of the image line, and a series of echo signals are obtained, which can be converted into a range-doppler plot. The data from the range-doppler plot may be combined with the azimuth angle of the corresponding image line to obtain a complete data set of range, azimuth, amplitude, and radial velocity information for the validated target. A map of the target may be generated based on a plurality of adjacent range cells having matching azimuth and radial velocity data, and the range cells may be contained within the same image line as well as within several adjacent image lines.

Thus, a map may be generated based on information from several consecutively arranged image lines covering several azimuth angle ranges, and data is typically obtained for a group comprising all image lines of a complete circumferential azimuth angle range from 0 ° to 360 ° before starting the calculation to define the map of the detected object. The quality of the map is highly dependent on the number of echo signals received by the radar system within a particular time period, and hence the number of signals transmitted within a time frame of an image line, and also on the repetition frequency of the scanning FMCW radar system. The main limiting factor in using a large number of transmitted signals and a high scan repetition frequency is the high requirements on signal processing speed and power.

The quality of the detection and tracking performance of the radar system is highly correlated with the quality of the generated map data, and thus with the speed at which the map data can be generated. Accordingly, there is a need for improved techniques of processing received radar scan signals to provide high quality map data that can be used for tracking and classifying objects including detected objects such as Unmanned Aerial Vehicles (UAVs). There is also a need for improved techniques for: a radar map is generated based on the received radar scan signals, and an object trajectory including an Unmanned Aerial Vehicle (UAV) trajectory is generated based on the radar map obtained from the received radar scan signals. There is also a need for improved techniques for classifying generated object trajectories, identifying trajectories that represent real Unmanned Aerial Vehicles (UAVs).

The UAV may be a known or cooperative UAV, and the UAV may also be an unknown or uncooperative UAV, such as a hostile UAV. The flight path of a known or cooperating UAV may be controlled from the control station by exchanging telemetry data, while there is no exchange telemetry data with an unknown or uncooperative UAV, and thus no control of the flight path of the uncooperative UAV.

Known UAV sensor systems include radar detection systems, which may include doppler-type radars, such as Frequency Modulated Continuous Wave (FMCW) radars. Such radar detection systems, however, are unable to distinguish between known or unknown UAVs.

Accordingly, there is a need for an improved sensor system that can track UAVs and distinguish between known controlled UAVs and unknown uncontrolled UAVs.

Disclosure of Invention

It is an object of the present disclosure to provide a system and method that can generate a complete data-type radar map containing range, azimuth, amplitude and radial velocity information of verified targets in a limited time, allowing for high scanning frequencies, and higher accuracy of the trajectory generated based on the map data.

According to a first aspect, there is provided a frequency modulated continuous wave, FMCW, radar system comprising:

one or more antennas configured to transmit and receive FMCW radar wave signals to scan an object within a full circle detection coverage; and

a processing circuit configured to: providing scan data based on the transmitted and received FMCW radar signals and the azimuth position of the antenna(s), and generating a radar map based on the obtained scan data; wherein

The processing circuitry is configured to:

providing scan data representing range cells within image lines of a circumferential radar image, wherein each radar image comprises a plurality of image lines defining a complete circumferential radar image, each image line corresponding to an azimuthal orientation, and wherein each image line comprises a plurality of range cells, each range cell corresponding to a range to the radar antenna(s), and wherein an object detected within the azimuthal orientation and range (range) relative to the radar antenna(s) is represented by a plurality of hitting range cells of the one or more image lines, and wherein each hitting range cell comprises data based on one or more radial velocities of the doppler frequency signals and data of the echo signal(s) energy, whereby the scan data of each hitting range cell comprises a range, a distance, and a distance to the detected object, Information of azimuth orientation, energy of returned radar wave signals, and one or more radial velocities or velocities; and wherein

The processing circuit is further configured to:

generating first type radar maps of the detected objects based on the obtained scan data, wherein each first type radar map is based on data from a plurality of adjacent hit range cells within one or more image lines of a first full circle radar image, each said first type radar map containing range, radial velocity and echo energy data for one or more detected objects;

generating second type radar maps of the detected objects based on the obtained scan data, wherein each second type radar map is based on data from a plurality of adjacent hit range cells within one or more image lines of the first full circle radar image, each said second type radar map containing azimuth, range, radial velocity, and return energy data for one or more detected objects; and generating a complete data type radar map by combining the first and second type radar maps with corresponding range data, each said complete data type radar map thereby containing azimuth, range, radial velocity and return energy data for one or more detected objects.

The use of processing power is optimized by dividing the process of generating data of a complete data type radar map into several steps, and the final map data can be obtained in a short time. This allows for a high scan rate, which in turn improves the quality of the final map data and higher accuracy of the trajectories generated based on the map data.

In a possible implementation form of the first aspect, the processing circuitry is configured to generate a first type radar map based on a grouping of adjacent hitting range cells within one or more image lines of the first full circle radar image with matching range and radial velocity data, and

generating a second type radar map based on groupings of neighboring hit range cells within one or more image lines of the first full circle radar image having matching range and azimuth data.

In a possible implementation form of the first aspect, the processing circuit is configured to initiate generation of the first type of radar map before initiating generation of the second type of radar map.

The generation of radial velocity data, which may be based on the generation of range-doppler plots, is the most computationally intensive process of computational processes required for the generation of data of a complete data type radar plot, whereas the generation of azimuth and range data requires less computational power. In order to obtain a complete data type radar map for a complete circumferential radar scan, the scan data needs to be provided and analyzed for a complete circumferential radar image. By initiating the generation of the first type of radar map and thereby the generation of radial velocity data before initiating the generation of the second type of radar map containing azimuth and range data, the total computation time required to obtain a map comprising all image line data of a circumferential radar image may be reduced.

In a possible implementation form of the first aspect, the processing circuit is configured to initiate generation of a first type of radar map when obtaining scan data of range cells of a first image line of the first full circle radar image.

In a possible implementation form of the first aspect, the processing circuit is configured to generate the first type radar map by: the range and radial velocity data (if any) received by the hit range cells for a first image line of the first full circle radar image is analyzed, and adjacent hit range cells having matching range and radial velocity data are grouped into a plurality of corresponding first type range radar maps (if any matches exist).

In a possible implementation form of the first aspect, the processing circuitry is configured to: continuing to generate a first type radar chart when obtaining the scanning data of the distance unit of the next image line of the first complete circumference radar image; and

continuing the generation of a first type of radar map until scan data is obtained for all image lines of the first complete circumferential radar image, thereby obtaining first type of radar maps of the first complete circumferential radar image, each of the first type of radar maps containing range, radial velocity and return energy data for one or more detected objects.

In a possible implementation form of the first aspect, the processing circuit is configured to continue the generation of the first type radar map by: the distance and radial velocity data obtained for the hit distance cells of different image lines are analyzed, and adjacent hit distance cells having matching distance and radial velocity data are grouped into a plurality of corresponding first type range radar maps.

In a possible implementation form of the first aspect, the processing circuitry is configured to: initiating generation of a second type of radar map when scan data for all image lines within the first full circle radar image has been obtained.

In a possible implementation form of the first aspect, the processing circuit is configured to generate the second type radar map by: the method comprises analyzing range and azimuth data received by hitting range units for image lines of a complete circular radar image and grouping adjacent hitting range units with matching range and azimuth data into a plurality of corresponding range radar maps of a second type, each radar map of the second type containing azimuth, range and return energy data of one or more detected objects, thereby obtaining a radar map of the second type of the complete circular radar image.

In a possible implementation form of the first aspect, the processing circuitry is configured to generate a complete data type radar map for the first complete circumferential radar image by: comparing range data of the obtained first and second types of radar maps of the complete circumferential radar image, and combining the first and second types of radar maps with matching range data into corresponding complete data type radar maps, whereby each of said complete data type radar maps contains azimuth, range, radial velocity and return energy data of one or more detected objects.

In a possible implementation form of the first aspect, the processing circuitry is configured to: an unconventional type radar map is selected from a complete data type radar map having velocity data representing positive and negative radial velocities.

In a possible implementation form of the first aspect, the processing circuitry is configured to: when the speed data of the full data type radar map represents a positive radial speed and a negative radial speed, and there is at least one predetermined minimum speed difference between the most positive radial speed and the most negative radial speed, the full data type radar map is selected as the unconventional type radar map.

In a possible implementation form of the first aspect, the processing circuitry is configured to: when the speed data of the full data type radar map represents only positive radial speed, the full data type radar map is selected as the conventional type radar map.

In a possible implementation form of the first aspect, the processing circuitry is configured to: selecting the full data-type radar map as the regular-type radar map when the speed data of the full data-type radar map represents a positive radial speed and a negative radial speed, and a maximum speed difference between a most positive radial speed and a most negative radial speed is less than the predetermined minimum speed difference.

In a possible implementation form of the first aspect, the system is configured to: scanning data is provided that can indicate radial velocity within a predetermined positive velocity range and within a predetermined negative velocity range, where the predetermined negative velocity range is the same size as the predetermined positive velocity range. The predetermined minimum speed difference between the most positive radial speed and the most negative radial speed may be at least 50%, such as at least 60%, such as at least 70% or such as at least 75% of the combined predetermined positive and negative speed ranges.

In a possible implementation form of the first aspect, the system is configured to: scanning data indicating radial velocities in the range of-30 m/s to +30m/s is provided. In a possible implementation form of the first aspect, the predetermined minimum speed difference between the most positive radial speed and the most negative radial speed is at least 45 m/s.

In a possible implementation form of the first aspect, the processing circuitry is configured to: generating trajectories of one or more Unmanned Aerial Vehicles (UAVs), wherein each UAV trajectory is based on at least two unconventional type radar maps having a match between corresponding data of the at least two unconventional type radar maps. Here, in a possible implementation form of the first aspect, the matching between corresponding data of the non-conventional type radar map includes matching between radial velocity data. In a possible implementation form of the first aspect, the matching between corresponding data of the non-conventional type radar map comprises matching between range data. In a possible implementation form of the first aspect, the matching between corresponding data of the non-conventional type radar maps comprises matching between echo energy data.

In a possible implementation form of the first aspect, the processing circuitry is configured to: when the unconventional type radar map does not match any unconventional type radar map, the unconventional type radar map is selected as the discarded unconventional type radar map.

In a possible implementation form of the first aspect, the processing circuitry is configured to: one or more bird tracks are generated, wherein each bird track is matched between corresponding data based on at least two, three, or four regular-type radar maps and/or a second-type radar map that is not matched to the first-type radar map. In a possible implementation form of the first aspect, the matching between corresponding data of the regular type radar map and/or the second type radar map comprises matching between range data. In a possible implementation form of the first aspect, the matching between corresponding data of the regular-type radar maps and/or the second-type radar maps comprises matching between radial velocity data. In a possible implementation form of the first aspect, the matching between corresponding data of the regular-type radar maps and/or the second-type radar maps comprises matching between echo energy data.

In a possible implementation form of the first aspect, the processing circuitry is configured to: determining whether there is a match between data of the discarded unconventional type radar map and corresponding data of the generated bird trajectory, including the discarded map in the bird trajectory if there is a match, and classifying the discarded map as representing a "hovering vehicle" trajectory if there is no match.

In a possible implementation form of the first aspect, the processing circuitry is configured to: determining, for the generated map of UAV trajectories:

an external energy sum, which is the sum of the echo energies of the range cells representing positive and negative radial velocity signals within an outer velocity range of the observed radial velocity range; and

the sum of the central energy, which is the sum of the echo energies of the range cells representing radial velocity signals within the central range of the observed radial velocity range, and/or the sum of the total energy, which is the sum of the echo energies of the range cells representing all radial velocity signals of the observed radial velocity range.

In a possible implementation form of the first aspect, the processing circuit is further configured to: classifying the UAV trajectory as either a real UAV trajectory or a non-real UAV trajectory based at least in part on a comparison of the determined external energy sum to the determined central energy sum or to the determined total energy sum. In a possible implementation form of the first aspect, the processing circuitry is configured to: classifying the UAV trajectory as either a real UAV trajectory or a non-real UAV trajectory based at least in part on a comparison between the determined total energy and a predetermined maximum energy representing a predetermined maximum radar scattering cross-section.

In a possible implementation form of the first aspect, the processing circuitry is configured to: updating the generated bird trajectories with new regular-type radar maps and/or second-type radar maps, wherein the second-type radar maps do not match the first-type radar maps,

determining a sum of echo energies for the updated bird trajectories from echo energy data contained in a radar map representing the bird trajectories,

determining the speed, acceleration, direction and curvature of the motion profile from the updated change trajectory data of the bird trajectory, an

Bird trajectories are classified as small bird trajectories, medium bird trajectories, large bird trajectories, bird flock trajectories or non-bird trajectories based on the determined echo energy sum and based on the determined velocity, acceleration, direction and curvature of the motion profile.

In a possible implementation form of the first aspect, the processing circuitry is configured to forward data representing the classified trajectory to a display unit in real-time, the display unit being configured to display the classified trajectory based on the received trajectory data. In a possible implementation form of the first aspect, the processing circuitry is configured to forward data representing the classified trajectory to a storage unit configured to store the classified trajectory data.

According to a first aspect, there is also provided a method of generating a radar map comprising radial velocity data, the method using a Frequency Modulated Continuous Wave (FMCW) radar system comprising one or more antennas configured to transmit and receive FMCW radar wave signals to scan a full circumference to detect objects within a coverage area, processing circuitry configured to obtain scan data based on the transmitted and received FMCW radar signals and an azimuthal position of the antenna(s), and to generate a radar map based on the obtained scan data; wherein, the method comprises the following steps:

obtaining scan data representing range cells within image lines of a circumferential radar image, wherein each radar image comprises a plurality of image lines defining a complete circumferential radar image, each image line corresponding to an azimuthal orientation, and wherein each image line comprises a plurality of range cells, each range cell corresponding to a range to the radar antenna(s), and wherein objects detected within azimuthal orientation and range relative to the radar antenna(s) are represented by a plurality of hitting range cells in the one or more image lines, and wherein each hitting range cell comprises data based on one or more radial velocities of doppler frequency signals and data of echo signal(s), whereby the scan data for each hitting range cell comprises a range, a distance, and a distance of the detected object, Information of azimuth orientation, energy of returned radar wave signals, and one or more radial velocities; and wherein the method further comprises:

generating second type radar maps of the detected objects based on the obtained scan data, wherein each second type radar map is based on data from a plurality of adjacent hit range cells within one or more image lines of the first full circle radar image, each second type radar map containing azimuth, range, and return energy data for one or more detected objects; and

a complete data type radar map is generated by combining first and second type radar maps having corresponding range data, each said complete data type radar map thereby containing azimuth, range, radial velocity and return energy data for one or more detected objects.

In a possible implementation form of the method of the first aspect, the generation of the first type of radar map is based on a grouping of adjacent hitting range cells within one or more image lines of the first complete circumferential radar image with matching range and radial velocity data, and the generation of the second type of radar map is based on a grouping of adjacent hitting range cells within one or more image lines of the first complete circumferential radar image with matching range and azimuth data.

In a possible implementation form of the method of the first aspect, the generation of the first type of radar map is initiated before the generation of the second type of radar map is initiated.

In a possible implementation form of the method of the first aspect, the generation of the first type of radar map is initiated when obtaining scan data of range cells of a first image line of the first full circle radar image.

In one possible implementation form of the method of the first aspect, the step of generating the first type radar map comprises:

analyzing the distance and radial velocity data obtained for the hit distance cell (if any) for the first image line, an

If there is a match, adjacent hit range cells having matching range and radial velocity data are grouped into a plurality of corresponding first type range radar maps.

In one possible implementation form of the method of the first aspect, the step of generating the first type radar map further comprises:

continuing to generate a first type radar chart when obtaining the scanning data of the distance unit of the next image line of the first complete circumference radar image; and

continuing the generation of the first type of radar map until scan data for all image lines of the first complete circumferential radar image have been obtained, thereby obtaining first type of radar maps of the first complete circumferential radar image, each of the first type of radar maps containing range, radial velocity and return energy data for one or more detected objects.

In a possible implementation form of the method of the first aspect, the continuous generation of the first type radar map is performed by: analyzing the distance and radial velocity data obtained by hitting the distance cells for different image lines, an

Adjacent hit range cells having matching range and radial velocity data are grouped into a plurality of corresponding first type range radar maps.

In a possible implementation form of the method of the first aspect, the generation of the second type radar map is initiated when the scan data of all image lines within the first full circle radar image has been obtained.

In one possible implementation form of the method of the first aspect, the generating of the second type radar map comprises:

analyzing range and azimuth data obtained by hitting range cells for image lines of a complete circular radar image, an

Grouping neighboring hit range cells having matching range and azimuth data into a plurality of corresponding range radar maps of a second type, thereby obtaining radar maps of a second type of a complete circumferential radar image, each radar map of the second type containing azimuth, range and echo energy data of one or more detected objects.

In a possible implementation form of the method of the first aspect, the complete data-type radar map of the first complete circumferential radar image is generated by:

comparing the distance data of the first and second type of radar maps of the obtained complete circumferential radar image, an

Combining the first and second type radar maps having matching range data into corresponding complete data type radar maps, whereby each of said complete data type radar maps contains azimuth, range, velocity and return energy data for one or more detected objects.

In a possible implementation form of the method of the first aspect, the method further comprises selecting the unconventional type radar map based on a complete data type radar map with velocity data representing positive and negative radial velocities.

In one possible implementation form of the method of the first aspect, the complete data-type radar map is selected as the non-conventional type radar map when the speed data of the complete data-type radar map represents a positive radial speed and a negative radial speed with at least one predetermined minimum speed difference between the most positive radial speed and the most negative radial speed.

In a possible implementation form of the method of the first aspect, the method further comprises: when the speed data of the full data type radar map represents only positive radial speed, the full data type radar map is selected as the conventional type radar map.

In one possible implementation form of the method of the first aspect, the full data-type radar map is selected as the regular-type radar map when the speed data of the full data-type radar map represents a positive radial speed and a negative radial speed, and a maximum speed difference between a most positive radial speed and a most negative radial speed is less than the predetermined minimum speed difference.

In a possible implementation form of the method of the first aspect, the FMCW radar system is configured to provide scanning data indicative of radial velocity within a predetermined positive velocity range and a predetermined negative velocity range, wherein the predetermined negative velocity range is the same size as the predetermined positive velocity range. In a possible implementation form of the method of the first aspect, the predetermined minimum speed difference between the most positive radial speed and the most negative radial speed is at least 50%, such as at least 60%, such as at least 70% or such as at least 75% of the combined predetermined positive and negative speed range. In a possible implementation form of the method of the first aspect, the FMCW radar system is configured to provide scanning data indicative of a radial velocity in a range of-30 m/s to +30 m/s. In a possible implementation form of the method of the first aspect, the predetermined minimum speed difference between the most positive radial speed and the most negative radial speed is at least 45 m/s.

In a possible implementation form of the method of the first aspect, the method further comprises generating trajectories of one or more Unmanned Aerial Vehicles (UAVs), wherein each UAV trajectory is based on at least two unconventional type radar maps having a match between corresponding data of the at least two unconventional type radar maps. In a possible implementation form of the method of the first aspect, the matching between corresponding data of two non-conventional types of radar maps comprises matching between radial velocity data.

In one possible implementation form of the method of the first aspect, the matching between corresponding data of two radar maps of a non-conventional type comprises matching between range data.

In a possible implementation form of the method of the first aspect, the matching between corresponding data of two non-conventional types of radar maps comprises matching between echo energy data.

In a possible implementation form of the method of the first aspect, the method further comprises: when the unconventional type radar map does not match any unconventional type radar map, the unconventional type radar map is selected as the discarded unconventional type radar map.

In one possible implementation form of the method of the first aspect, the method further comprises generating one or more bird tracks, wherein each bird track is based on a match between corresponding data of at least two, three or four regular-type radar maps and/or a second-type radar map that does not match the first-type radar map. In a possible implementation form of the method of the first aspect, the matching between corresponding data of the regular type radar map and/or the second type radar map comprises matching between range data. In a possible implementation form of the method of the first aspect, the matching between corresponding data of the regular type radar map and/or the second type radar map comprises matching between radial velocity data. In a possible implementation form of the method of the first aspect, the matching between corresponding data of the regular type radar map and/or the second type radar map comprises matching between echo energy data.

In one possible implementation form of the method of the first aspect, the method further comprises determining whether there is a match between data of the discarded unconventional type radar map and corresponding data of the generated bird trajectory, including the discarded map in the bird trajectory if there is a match, and classifying the discarded map as representing a "hover stop" trajectory if there is no match.

In one possible implementation form of the method of the first aspect, the method further comprises: determining, for the generated map of UAV trajectories:

In a possible implementation form of the method of the first aspect, the method further comprises classifying the UAV trajectory as a real UAV trajectory or a non-real UAV trajectory based at least in part on a comparison of the determined external energy sum to the determined central energy sum and/or to the determined total energy sum. In a possible implementation form of the method of the first aspect, classifying the UAV trajectory as a real UAV trajectory or a non-real UAV trajectory is further based at least in part on the determined total energy sum in comparison to a predetermined maximum energy representing a predetermined maximum radar scattering cross-section.

In one possible implementation manner of the method of the first aspect, the method further includes:

the bird trajectories generated are updated with new regular type radar maps and/or second type radar maps that do not match the first type radar maps,

determining, for the updated bird trajectory, that a sum of echo energies from echo energy data is contained by a radar map representing the bird trajectory,

Bird trajectories are classified as small bird trajectories, medium bird trajectories, large bird trajectories, bird flock trajectories or non-bird trajectories based on the determined sum of the echo energies and based on the determined speed, acceleration, direction and curvature of the motion profile.

In a possible implementation form of the method of the first aspect, the method further comprises: data for the classified trajectories is forwarded in real time to a display unit, and the classified trajectories are displayed on the display unit based on the received trajectory data. In a possible implementation form of the method of the first aspect, the method further comprises: data for the classified trajectory is forwarded to a storage unit, and the classified trajectory data is stored by the storage unit.

It is an object of the present invention to provide a system for generating a radar map that can be used to generate object trajectories with a high probability of representing an Unmanned Aerial Vehicle (UAV). It is a further object of the invention to provide a system for generating object trajectories based on obtained radar maps.

According to a second aspect, there is provided a Frequency Modulated Continuous Wave (FMCW) radar system comprising:

one or more antennas configured to transmit and receive FMCW radar wave signals to scan an object, such as an Unmanned Aerial Vehicle (UAV), within a full circle detection coverage; and

a processing circuit configured to:

providing scan data based on the transmitted and received FMCW radar signals and the azimuth position of the antenna(s); and

generating complete data type radar maps based on the obtained scan data, each of the complete data type radar maps containing azimuth, range, radial velocity, and received echo energy data for one or more detected objects; wherein

The processing circuit is further configured to:

an unconventional type radar map is selected from the complete data type radar maps having velocity data representing positive and negative radial velocities within the range of observed radial velocities and having at least one predetermined minimum velocity difference between the observed radial velocity having the largest positive radial value and the observed radial velocity having the largest negative value in absolute value.

In a possible implementation form of the second aspect, the processing circuit is configured to generate one or more object trajectories or Unmanned Aerial Vehicle (UAV) trajectories, wherein each object/UAV trajectory is based on at least two unconventional type radar maps having a match between corresponding data of the at least two unconventional type radar maps.

In a possible implementation form of the second aspect, the system is configured to provide the scan data indicative of the radial velocity within a predetermined positive velocity range and a predetermined negative velocity range, wherein the predetermined negative velocity range is the same size as the predetermined positive velocity range.

In a possible implementation form of the second aspect, the predetermined minimum speed difference between the observed radial speed with the largest positive value and the observed radial speed with the largest negative value in absolute value is at least 50%, such as at least 60%, such as at least 70% or such as at least 75% of the combined predetermined positive and negative speed range.

In a possible implementation form of the second aspect, the system is configured to provide scan data indicative of a relative velocity in a range of-30 m/s to +30 m/s.

In a possible implementation form of the second aspect, the predetermined minimum speed difference between the observed radial speed having the largest positive value and the observed radial speed having the largest negative value in absolute value is at least 45 m/s.

In a possible implementation form of the second aspect, the matching between the corresponding data comprises matching between radial velocity data.

In a possible implementation form of the second aspect, the matching between corresponding data comprises matching between distance data.

In a possible implementation form of the second aspect, the matching between the corresponding data comprises matching between echo energy data.

In a possible implementation form of the second aspect, the processing circuit is configured to: for the generated map of object/UAV trajectories, determining:

a sum of central energies, which is the sum of echo energies of range cells representing radial velocity signals within a central range of an observed radial velocity range, and/or

The total energy sum, which is the sum of the echo energies of the range cells of all radial velocity signals representing the range of observed radial velocities.

In a possible implementation form of the second aspect, the processing circuit is configured to: classifying the object/UAV trajectory as either a real UAV trajectory or a non-real UAV trajectory based at least in part on a comparison of the determined external energy sum to the determined central energy sum and/or to the determined total energy sum.

In a possible implementation form of the second aspect, the processing circuit is configured to: when the determined external energy sum is below a predetermined fraction of the determined central energy sum, such as below 1/1000 of the central energy sum, classifying the object/UAV trajectory as a non-real UAV trajectory.

In a possible implementation form of the second aspect, the processing circuit is configured to: classifying the object/UAV trajectory as a non-real UAV trajectory when the determined external energy sum is below a predetermined fraction of the determined total energy sum.

In a possible implementation form of the second aspect, the processing circuit is configured to: classifying the object/UAV trajectory as either a real UAV trajectory or a non-real UAV trajectory based at least in part on the determined total energy and a comparison to a predetermined maximum energy representing a predetermined maximum radar scattering cross-section.

In a possible implementation form of the second aspect, the processing circuit is configured to: when the determined sum of total energy is above a value representing a predetermined maximum radar scattering cross-section (such as a maximum radar scattering cross-section of 1 m)²) Is determined, the object/UAV trajectory is classified as a non-UAV or large UAV trajectory.

In a possible implementation form of the second aspect, the processing circuit is configured to: classifying the object/UAV trajectory as a real UAV trajectory when the determined external energy sum is above a predetermined fraction of the determined central energy sum and/or above a predetermined fraction of the determined total energy sum, and when the determined total energy sum is below a predetermined maximum energy representing a predetermined maximum radar scattering cross-section.

In a possible implementation form of the second aspect, the processing circuit is configured to: forwarding data representing the classified trajectory in real-time to a display unit configured to display the classified trajectory based on the received trajectory data.

In a possible implementation form of the second aspect, the processing circuitry is configured to forward data representing the classified trajectory to a storage unit configured for storing the classified trajectory data.

In a possible implementation form of the second aspect, the processing circuit is configured to:

providing scan data representing range cells within image lines of a circumferential radar image, wherein each radar image comprises a plurality of image lines defining a complete circumferential radar image, each image line corresponding to an azimuthal orientation, and wherein each image line comprises a plurality of range cells, each range cell corresponding to a range to the radar antenna(s), and wherein an object detected within azimuthal orientation and range relative to the radar antenna(s) is represented by a plurality of hit range cells of one or more image lines, and wherein each hit range cell comprises data based on one or more radial velocities of the doppler frequency signal and energy of the echo signal(s), whereby for each hit range cell the scan data comprises a range, a distance, and a distance of the detected object, Azimuth orientation, radar return signal energy, and one or more radial velocities.

In a possible implementation form of the second aspect, the processing circuitry is configured to generate the complete data type radar map based on a grouping of adjacent hit range cells within one or more image lines of the complete circumferential radar image having matching range, azimuth and radial velocity data.

It will be appreciated that the processing system of the radar system of the second aspect may be configured to generate a complete data type radar map in accordance with one or more possible implementations of the first aspect. It will also be appreciated that the processing system of the radar system of the second aspect may be configured to generate and/or classify object trajectories according to one or more possible implementation forms of the first aspect.

It is an object of the present invention to provide a system that can identify object trajectories representing real Unmanned Aerial Vehicles (UAVs).

According to a third aspect, there is provided a Frequency Modulated Continuous Wave (FMCW) radar system comprising:

a processing circuit configured to:

providing scan data based on the transmitted and received FMCW radar signals and the azimuthal position of the antenna(s), the scan data representing range cells within image lines of a circumferential radar image, wherein each radar image contains a plurality of image lines defining a complete circumferential radar image, each image line corresponding to an azimuthal orientation, and wherein each image line contains a plurality of range cells, each range cell corresponding to a range to the radar antenna(s), and wherein objects detected within the azimuthal orientation and range relative to the radar antenna are represented by a plurality of hit range cells within the one or more image lines, and wherein each hit range cell contains data based on one or more radial velocities of the Doppler frequency signals and energy of the echo signal(s), whereby for each hit range cell, the scan data contains information of range, azimuthal orientation, radar echo signal energy, and one or more radial velocities of the detected object; wherein

The processing circuit is further configured to:

generating complete data type radar maps based on the obtained range cell scan data, each of the complete data type radar maps containing azimuth, range, radial velocity, and received echo energy data for one or more detected objects;

selecting an unconventional type radar map from the complete data type radar map, the unconventional type radar map having velocity data representing positive and negative radial velocities within the observed range of radial velocities;

generating one or more object trajectories or Unmanned Aerial Vehicle (UAV) trajectories, wherein each object/UAV trajectory is based on at least two unconventional type radar maps having a match between corresponding data of the at least two unconventional type radar maps.

For the generated object/UAV trajectory map, determining:

an external energy sum, which is the sum of the echo energies of range cells representing positive and negative radial velocity signals within first and second external velocity ranges outside the central range of the observed radial velocity range;

a central energy sum which is the sum of the echo energies of range cells representing radial velocity signals within the central range of the observed radial velocity range and/or a total energy sum which is the sum of the echo energies of range cells representing all radial velocity signals of the observed radial velocity range; and is

Wherein the processing circuit is further configured to:

classifying the object/UAV trajectory as either a real UAV trajectory or a non-real UAV trajectory based at least in part on a comparison of the determined external energy sum to the determined central energy sum, and/or to the determined total energy sum.

In a possible implementation form of the third aspect, the processing circuit is configured to: classifying the object/UAV trajectory as either a real UAV trajectory or a non-real UAV trajectory based at least in part on the determined total energy and a comparison to a predetermined maximum energy value representative of a predetermined maximum radar scattering cross-section.

In a possible implementation form of the third aspect, the processing circuit is configured to: classifying the object/UAV trajectory as a real UAV trajectory when the determined external energy sum is above a predetermined fraction of the determined central energy sum and/or above a predetermined fraction of the determined total energy sum, and when the determined total energy sum is below a predetermined maximum energy representing a predetermined maximum radar scattering cross-section.

In a possible implementation form of the third aspect, the processing circuit is configured to: when the determined external energy sum is below a predetermined fraction of the determined central energy sum, such as below 1/1000 of the central energy sum, classifying the object/UAV trajectory as a non-real UAV trajectory.

In a possible implementation form of the third aspect, the processing circuit is configured to: classifying the object/UAV trajectory as a non-real UAV trajectory when the determined external energy sum is below a predetermined fraction of the determined total energy sum.

In a possible implementation form of the third aspect, the processing circuit is configured to: when the determined sum of total energy is above a value representing a predetermined maximum radar scattering cross-section (such as a maximum radar scattering cross-section of 1 m)²) Is determined, the object/UAV trajectory is classified as a non-UAV or large UAV trajectory.

In a possible implementation form of the third aspect, the processing circuitry is configured to determine a radial speed boundary of the central speed range, and to determine a radial speed boundary of a first outer speed range comprising a negative radial speed signal, and a radial speed boundary of a second outer speed range comprising a positive radial speed signal, wherein the determination of the speed boundaries of the first speed range and the second speed range is based on the observed radial speed boundaries of the entire speed range and the central speed range.

In a possible implementation form of the third aspect, the processing circuitry is configured to determine the radial velocity boundary of the central velocity range based on an observed variation of an energy level of the returned radar signal with radial velocity.

In a possible implementation form of the third aspect, the processing circuit is configured to: determining that the observed energy level decreases to a local minimum on both sides of a center speed of the observed speed range, and determining a radial speed boundary of the center speed range as a radial speed at which the observed energy level on both sides of the center speed has increased by a predetermined factor from the observed local minimum. Preferably, the predetermined incremental factor is about 2 or 3 dB.

In a possible implementation form of the third aspect, the processing circuit is configured to: a complete data-type radar map is generated based on groupings of neighboring hit range cells within one or more image lines of a complete circumferential radar image having matching range, azimuth, and radial velocity data.

In a possible implementation form of the third aspect, the processing circuit is configured to: forwarding data representing the classified trajectory in real-time to a display unit, wherein the display unit is configured to display the classified trajectory based on the received trajectory data.

In a possible implementation form of the third aspect, the processing circuit is configured to forward data representing the classified trajectory to a storage unit configured for storing the classified trajectory data.

It will be appreciated that the processing system of the radar system of the third aspect may be configured to generate a complete data type radar map in accordance with one or more possible implementations of the first aspect. It will also be appreciated that the processing system of the radar system of the third aspect may be configured to generate and/or classify object trajectories according to one or more possible implementation forms of the first aspect.

It is an object of the present invention to provide a system that can both track UAVs and distinguish between tracked UAVs.

According to a fourth aspect, there is provided an Unmanned Aerial Vehicle (UAV) system comprising:

a control station for controlling a first cooperative Unmanned Aerial Vehicle (UAV), the control station configured for exchanging telemetry data with the first UAV, including data instructing the first UAV to follow a flight path based on flight plan instructions received from the control station, and the first UAV may be provided with a transponder containing identification Information (ID) for the first UAV, and the first UAV and the control station may be configured for exchanging transponder data;

a radar system or ground-based radar system configured to scan for objects within a detection coverage area and to provide scan data indicative of objects detected within the coverage area; and

a processing circuit configured to:

generating a radar map of one or more detected objects based on scan data received from the radar system;

generating and storing one or more UAV object trajectories based on the matched radar map, each UAV object trajectory containing object data corresponding to data of the matched map;

receiving telemetry data and/or transponder data of the first UAV;

determining for each UAV object trajectory whether there is a match between data of the UAV object trajectory and corresponding telemetry data and/or transponder data of the first UAV; and

the UAV of the UAV object trajectory is classified as a first cooperating UAV when the corresponding data of the UAV object trajectory and the received telemetry data and/or transponder data satisfy a predetermined matching condition, and classified as a second non-cooperating UAV when the predetermined matching condition is not satisfied.

By comparing the telemetry data and/or transponder data with trajectory data based on radar scan data, the obtained trajectory can be divided into a known UAV trajectory representing a controllable UAV and an unknown UAV trajectory representing an uncontrollable UAV.

In a possible implementation form of the fourth aspect, the radar system comprises a doppler-type radar, such as a Frequency Modulated Continuous Wave (FMCW) radar.

In a possible implementation form of the fourth aspect, the processing circuitry is configured to generate UAV object trajectory containing data representing position, radial velocity and size of tracked objects based on data of the matched radar map obtained from the received scan information. The position data may include range and azimuth angles relative to the radar system, the velocity data may be radial/doppler velocity, and the size data may be determined based on the energy of the echographic signals within the map forming the object trajectory.

In a possible implementation form of the fourth aspect, the first cooperative UAV comprises a Global Positioning System (GPS) and the telemetry data forwarded by the control station to the processing circuitry for the first cooperative UAV comprises location data based on the GPS data. Such location data may include distance, azimuth, altitude, speed, and/or direction of travel relative to the control station.

In a possible implementation form of the fourth aspect, the first cooperating UAV contains a transponder with ID data, the transponder data forwarded by the first UAV to the control station representing the ID data and possibly also representing position data, such as altitude or altitude data.

In a possible implementation form of the fourth aspect, the predetermined matching condition to be fulfilled between the stored trajectory data and the received telemetry data and/or transponder data comprises a match between the location data.

In a possible implementation form of the fourth aspect, the predetermined matching condition to be satisfied between the stored trajectory data and the received telemetry data and/or transponder data comprises a match between speed data and/or a match between size data.

In a possible implementation form of the fourth aspect, the processing circuit is configured to determine when a matching condition for a set of corresponding data is met based on a predetermined threshold difference between the matched data.

In one possible implementation form of the fourth aspect, then, when classifying the UAV of the UAV object trajectory as a second non-cooperative UAV, the control station is configured to:

generating and forwarding flight plan instructions to the first cooperative UAV based at least in part on object data from the object trajectory of the second non-cooperative UAV.

In one possible implementation form of the fourth aspect, then, when classifying the UAV of the UAV object trajectory as a second non-cooperative UAV, the control circuitry is configured to:

generating and forwarding action instructions to the first UAV based on information obtained from an object trajectory of the second UAV, and wherein

The first cooperative UAV is configured to perform an action based at least in part on the received action instruction.

In a possible implementation form of the fourth aspect, the control station is configured to: generating and forwarding flight disturbance maneuver instructions to the first cooperative UAV based on information obtained from the object trajectory of the second non-cooperative UAV.

In one possible implementation form of the fourth aspect, the first cooperating UAV is configured to: performing a flight route or flight plan intervention maneuver for the second non-cooperating UAV based on the received flight intervention maneuver instruction.

In a possible implementation form of the fourth aspect, the flight route or flight plan interfering action to be performed may steer the first cooperative UAV towards the second non-cooperative UAV to cause a collision between the first UAV and the second UAV.

In a possible implementation form of the fourth aspect, the control station is configured to generate flight plan instructions and/or action instructions based on position data obtained from an object trajectory of the second non-cooperative UAV. Here, the position data may include a distance and an azimuth angle with respect to the radar system.

In a possible implementation form of the fourth aspect, the control station is configured to: generating flight plan instructions and/or action instructions based on dimensional data obtained from the object trajectory of the second non-cooperative UAV. Here, the sizing data may be determined based on an echo scan signal energy within a graph forming an object trajectory of the second uncooperative UAV.

In a possible implementation form of the fourth aspect, the control station is configured to generate flight plan instructions and/or action instructions for the first cooperative UAV based at least in part on the received telemetry data and/or transponder data of the first cooperative UAV.

In a possible implementation form of the fourth aspect, the first cooperating UAV includes a camera, and the first UAV is configured to send telemetry data comprising a video signal to the control station, and the control station is configured to generate the flight path information based at least in part on the received video signal.

In a possible implementation form of the fourth aspect, the control station is configured to: generating and forwarding flight plan information based on position data obtained from object trajectories of a second non-cooperating UAV until the second non-cooperating UAV is detected within the received video signal.

In a possible implementation form of the fourth aspect, the control station is then configured to generate and forward flight plan information based on the received video signal when a second non-cooperating UAV is detected within the received video signal.

In one possible implementation form of the fourth aspect, the processing circuit is configured to:

generating complete data type radar maps based on the obtained scan data, each of the complete data type radar maps containing azimuth, range, radial velocity, and received echo energy data for one or more detected objects;

generating one or more UAV object trajectories, wherein each UAV object trajectory is based on at least two unconventional type radar maps having a match between corresponding data of the at least two unconventional type radar maps.

In a possible implementation form of the fourth aspect, the selected non-conventional type of radar map has velocity data representing positive and negative radial velocities, and has at least one predetermined minimum velocity difference between an observed radial velocity having a maximum positive value and an observed radial velocity having a negative value with a maximum absolute value.

In a possible implementation form of the fourth aspect, the generated UAV trajectory is based at least in part on a radar map with matching radial velocity data.

In a possible implementation form of the fourth aspect, the generated UAV trajectory is based at least in part on a radar map with matching range data.

In a possible implementation form of the fourth aspect, the generated UAV trajectory is based at least in part on a radar map with matching echo energy data.

In a possible implementation form of the fourth aspect, the processing circuit is configured to match the echo energy data of two unconventional types of radar maps by:

determining the sum of the echo energies of all radial velocity signals for each of the two non-conventional type radar maps that are matched, an

It is determined whether there is a match between the sums of the acquired echo energies.

determining, for each of the two unconventional type radar maps that are matched, a sum of central echo energies corresponding to a central radial velocity span within the observed range of radial velocities, an

It is determined whether there is a match between the sums of the acquired center echo energies.

It is to be understood that the processing system of the radar system of the fourth aspect may be configured to generate a complete data type radar map according to one or more possible implementation forms of the first aspect. It is further to be understood that the processing system of the radar system of the fourth aspect may be configured to generate and/or classify object trajectories according to one or more possible implementation forms of the first aspect.

It is to be understood that the radar system of the first aspect may comprise possible implementations of the radar system of the second, third and/or fourth aspect, which implementations have not been included in the first aspect. Furthermore, the radar system of the second aspect may comprise possible implementations of the radar system of the first, third and/or fourth aspect, which implementations have not been included in the second aspect. Furthermore, the radar system of the third aspect may comprise possible implementations of the radar system of the first, second and/or fourth aspect, which implementations have not been included in the third aspect. Similarly, the radar system of the fourth aspect may comprise possible implementations of the radar system of the first, second and/or third aspect, which implementations have not been included in the fourth aspect.

The foregoing and other objects are achieved by the features of the independent claims. Further forms of implementation are apparent from the dependent claims, the description and the accompanying drawings. These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter.

Drawings

In the following detailed part of the disclosure, the invention will be explained in more detail with reference to exemplary embodiments shown in the drawings, in which:

FIG. 1a is a schematic block diagram showing the basic structure of a scanning radar system according to an exemplary embodiment;

fig. 1b is a block diagram illustrating functional components of a Frequency Modulated Continuous Wave (FMCW) transceiver that is part of the radar system of fig. 1a, according to an example embodiment.

FIG. 2 shows a radar image with image lines and range cells according to an example embodiment;

fig. 3 shows the transmission of a radar wave in the form of a Frequency Modulated Continuous Wave (FMCW), according to an exemplary embodiment;

figures 4a and 4b show range-doppler plots with micro-doppler signatures according to an exemplary embodiment;

FIG. 5 is a flowchart illustrating the generation of data for graph generation in accordance with an illustrative embodiment;

FIG. 6 is a flow chart illustrating the generation of a first type of radar map containing range, radial velocity, and echo energy information in accordance with an illustrative embodiment;

FIG. 7 is a flowchart illustrating the generation of a second type of radar map containing range, azimuth, and return energy information in accordance with an illustrative embodiment;

FIG. 8 is a flowchart illustrating the overall processing steps performed to achieve classification of detected objects in accordance with an exemplary embodiment;

FIG. 9 is a flowchart illustrating the generation of a full type radar map containing range, azimuth, radial velocity, and echo energy information in accordance with an illustrative embodiment;

FIG. 10 is a flow diagram illustrating a division of an obtained full type radar map into regular and non-regular maps based on speed information in accordance with an exemplary embodiment;

FIG. 11 is a general flow diagram illustrating the generation of Unmanned Aerial Vehicle (UAV) trajectories according to an exemplary embodiment;

FIG. 12 is an overall flow diagram illustrating the generation of bird trajectories according to an exemplary embodiment;

FIG. 13 is a detailed flow chart illustrating the generation of bird trajectories according to an exemplary embodiment;

FIG. 14 is a detailed flow chart illustrating the generation of Unmanned Aerial Vehicle (UAV) trajectories according to an exemplary embodiment;

FIG. 15 is a flowchart illustrating classification of an obtained trajectory according to an exemplary embodiment;

FIG. 16 is a graph illustrating reflected energy as a function of radial velocity of a tracked Unmanned Aerial Vehicle (UAV), according to an exemplary embodiment;

fig. 17 is a schematic block diagram showing a basic structure of an Unmanned Aerial Vehicle (UAV) system according to an exemplary embodiment.

FIG. 18 is a general flowchart illustrating processing steps including graph generation, trajectory generation and classification of detected objects based on data obtained by the system of FIG. 17, according to an exemplary embodiment;

FIG. 19 is a general flow diagram illustrating the generation of Unmanned Aerial Vehicle (UAV) trajectories based on data obtained by the system of FIG. 17, according to an exemplary embodiment; and

fig. 20 is a block diagram illustrating the exchange of signals between portions of the system of fig. 17, according to an example embodiment.

Detailed Description

FIG. 1a is a schematic block diagram illustrating the basic structure of a scanning radar system according to an exemplary embodiment. The system includes a Frequency Modulated Continuous Wave (FMCW) radar system 101 electrically connected to a computer system 102. The generated output data may be transmitted to the external command and control system 103, where the data may be transmitted by real-time data streaming, where, for example, extensible markup language (XML) may be used for streaming.

The FMCW radar system 101 comprises a transmitting horn 110 and a receiving horn 111, wherein a splitting plane 113 is arranged between the two

antennas

110 and 111 to prevent spurious reflections. The

antennas

110, 111 are surrounded by a radome 114 made of plastic, which does not produce or has a very low reflection of radar waves, so that interference by doppler shifts is avoided. The splitting plane 113 is arranged very close to the radome 114, also to prevent spurious reflections. The

antennas

110, 111 are mounted to an upright support 112a that is rotatably mounted to a horizontal support 112b, wherein the horizontal support is configured to rotate the upright 112a with the

antennas

110, 111 at a rotational speed of 45 revolutions per minute (rpm). At the horizontal support 112b an azimuth encoder is provided, which is configured for encoding and transmitting the degree of rotation of the

antennas

110, 111, and thereby the azimuth, with a very high accuracy. The antenna system 101 is configured to transmit FMCW radar signals in the range of 8, 7 to 10GHz at a transmit power of approximately 4 watts. The

feedhorns

110 and 111 cover almost square beam windows having a beam height of about 10 ° and a beam width of about 10 °. This configuration of the antenna system 101 yields a detection coverage of about 1 km. The beam width of 10 ° is much wider than normal for an FMCW radar system, where the beam width is typically around 1 °. By using a wide beam width of 10 °, the detected target or object will be exposed to the transmitted radar signal for a longer time, resulting in more time being required for data processing to determine the doppler shift. Targets or objects exposed to radar signals may include one or more Unmanned Aerial Vehicles (UAVs) 105 and one or more birds 106.

The FMCW radar system 101 comprises electronic front-end circuitry 116, also mounted to the upright 112a, for feeding the transmitting antenna 110 and receiving radar return signals received by the receiving antenna 111. The front end circuit 116 is surrounded by an aluminum shield 117 which shields electrical noise signals from entering the circuit 116 and which also acts as a heat sink. The front-end circuit 116 is electrically connected to the back-end circuit as part of the azimuth encoder to transmit the azimuth angle. The front-end circuitry 116 and the back-end circuitry are electrically connected to the computer system 102, whereby the electronic circuitry of the computer system 102, the front-end circuitry 116 and the back-end circuitry together provide processing circuitry for processing signals forwarded to and received from the antenna system 101 and for generating radar maps. The processing circuitry may also perform processing for generating object trajectories based on the graph and for classifying objects of the trajectories.

The electronic signals are transmitted from the front end circuitry 116 through a fiberglass cable to the rotary joint at the horizontal support 112b, which is connected to the computer system 102 through a cable.

Functional components of the front-end circuitry 116 and the back-end circuitry are shown in fig. 1b, which is a block diagram of electronic and radar components showing a Frequency Modulated Continuous Wave (FMCW) transceiver in accordance with an exemplary embodiment. The transmission of the radar wave signal is controlled by a digital control word received by an FMCW frequency sweep generator 116a, which contains a voltage controlled oscillator VCO, and generates a single ramp-shaped FMCW transmission frequency sweep signal, which is fed to a-3 dB coupler 116 b. The coupler 116b branches a portion of the signal to the mixing stage 116f, the main portion of the swept frequency signal is forwarded to the digital attenuator 116c for azimuth attenuation, and the output of the azimuth attenuator 116c is sent to the power amplifier 116 d. Finally, the amplified swept Radio Frequency (RF) signal is radiated as an electromagnetic radar wave signal by the transmitting antenna 110. The returned radar wave signal is received by a receiving antenna 111, which converts the received electromagnetic waves back into RF signals. The received RF signal is amplified by a low noise amplifier 116e and the amplified received signal is fed to a mixing stage 116 f. The output of the mixing stage 116f is the difference between the transmitted signal and the received signal. The difference signal from the mixing stage 116f is filtered by a low pass frequency filter 116g to block unwanted mixing frequencies and then amplified at an amplifier 116h before being converted from an analog signal to a digital signal at an analog-to-digital converter 116 i. The digital signal output from the converter 116i is a signal output from the front-end circuit 116 and contains information that can determine the distance, radial velocity, and echo energy of a detected object or target.

Then, by the "add azimuth information" back-end circuit 116j, the azimuth information from the encoder is added to the output from the converter 116i together with the attenuator settings from the attenuator 116c, whereby digital scan data containing information from which the azimuth angle, distance, radial velocity and echo energy of the object or target under inspection can be determined. In an alternative embodiment, the FMCW radar system 101 is also configured for scanning in altitude, and for this embodiment elevation angle information may also be added to the output of the converter 116i at the back-end circuit 116 j.

The digital signal output from the converter 116i, and thus the digital scan data output from the back-end circuit 116, contains information of the output difference signal from the mixing stage 116f, which contains the following information: the amplitude of the received echo signals, the time delay (Δ t) between the transmitted and received signals, and the beat or frequency difference (Δ f) between the rising edges of the transmitted and received signals. The beat frequency also includes a Doppler shift f_DAnd using the results of several successive frequency sweep signals, the range and doppler shift or doppler velocity can be determined by using a dual fourier transform on the digitally converted frequency sweep signals, and the results can be presented in a range-doppler plot.

The scanning operation and presentation of the data in the range-doppler plot is further illustrated in figures 2, 3 and 4.

The FMCW radar system 101 scans an object over a full circumferential range of 360 ° and obtains scan data with reference to a radar image as shown in fig. 2. Fig. 2 shows an exemplary embodiment of a radar image 200, which is divided into 80 image lines 201 to cover an omnidirectional angular range 204 of 360 ° of the image. The radar 101 is rotated at a speed of 45 revolutions per minute (rpm), wherein a complete radar image 200 is obtained for each rotation. Each image line is divided into 1356 range cells 203 and covers an azimuth range 202 of 4, 5 °. For each image line, the radar 101 transmits 100 FMCW frequency sweep signals 205, which are mixed with the corresponding returned radar wave signals, and generates a doppler plot representing the corresponding image line 201 for a particular azimuthal range from a mixed set of radar signals (where each set represents all 100 FMCW frequency sweep signals).

The

antennas

110, 111 of the radar system 101 have a beam width of about 10 °, which is about twice the azimuth extent of the image line 201. This allows the transmission and reception of 100 FMCW frequency sweep signals within the time frame in which the image line 201 is covered by the

antennas

110, 111 during rotation of the radar system 101.

As shown in fig. 3, the FMCW frequency-swept signal may have a single-slope shape, which shows several FMCW radar waves 300 in time-frequency diagrams 301, 302 according to an exemplary embodiment.

Figure 4a shows a range-doppler plot 400 with a micro-doppler signature, where range is along the x-axis 401 and radial velocity/doppler velocity is along the y-axis 402, with maximum radial velocities of +30m/s and-30 m/s, according to an exemplary embodiment. The range-doppler plot 400 of figure 4a shows an example of the spread in radial velocity for 6 range cells. For range unit 4, there is an extension in the observed radial velocity, represented by Δ 403, where full dot 404 represents the radial velocity at which the received echo signal has the highest amplitude or echo energy, and the smaller dots on each side of full dot 404 represent the radial velocity at which there is less amplitude or echo energy in the received echo signal. For range cell 4, the main contribution to the radial velocity represented by point 404 may come from the torso (torso) of a target such as a bird or helicopter and represents the radial/doppler velocity of the target, while the velocity sidebands observed around point 404 are referred to as micro-doppler signatures and may represent the flapping motion of the wings of the bird or the propeller blades of the helicopter.

The range-doppler plot may be computed as a range-doppler matrix, where the columns of the matrix represent the velocity detection span for a given single target range or range cell, and the rows represent the range detection span for a given single target velocity range. The velocity column may be referred to as the so-called doppler signature, while the range-doppler plot/matrix consists of the doppler signatures of all individual target ranges or range cells. Each cell within the range-doppler matrix, called bin, then represents a certain range cell, as well as a certain radial velocity range. This is illustrated in fig. 4b, which shows two

doppler signatures

405a and 405b for two different target ranges or range bins. For each interval of the

doppler signature

405a and 405b, the amount of received echo energy is represented by one or more "x". The

doppler signatures

405a and 405b have velocity sidebands or micro-doppler signatures on both sides of the main doppler velocity, where the interval with "xx" may represent the torso of the target and the interval with one "x" may represent the flapping or rotating motion of the target.

The doppler signature 405a includes three adjacent bins of observed radial velocity with a given radial velocity spread Δ 406 a. Notably, all observed velocities within the extension 406a are positive, indicating that all target portions of a given echo signal are moving away from the radar system 101 at the time of observation. The doppler signature 405b includes three adjacent bins of observed radial velocity with a given radial velocity spread Δ 406 b. Notably, the observed velocity within extension 406b is both positive and negative, with the highest echo energy observed for the interval with negative radial velocity, indicating that the torso of the target is moving in the direction of radar system 101 at the time of observation, while other portions of the target are moving in both radial directions of radar system 101. The

doppler signatures

405a, 405b of figure 4b each contain 8 bins, but for a radar system 101 configured with 100 FMCW frequency sweep signals (205) per image line 201, the doppler signature obtained has 100 bins, 50 for positive radial velocity and 50 for negative radial velocity. There are 1356 range cells 203 within each image line 201, and then for each image line 201, a range-doppler plot/matrix is generated containing 100 rows and 1356 columns for each image line 201. For 80 image lines 201, then 80 range-doppler plots/matrices must be generated to cover the complete radar image 200, where each range-doppler plot/matrix corresponds to an image line within a defined 4, 5 ° azimuth range. Notably, each bin where there is a hit target contains information of the amplitude or echo energy of the received radar signal, and the location of the bin within the range-doppler plot/matrix gives the radial velocity of the target and the range to the target, while the azimuth is given by the location of the image line 201 within the radar image 200.

An important feature of the present disclosure is to divide the process of generating data for a complete data type radar map into several steps, thereby optimizing the use of processing power. This is illustrated in fig. 5, 6 and 7.

FIG. 5 is a flowchart illustrating the generation of data for graph generation in accordance with an illustrative embodiment. The overall process of fig. 5 is referred to as "graph input data generation" 500 and begins with digital scan data 501 output from the front-end and back-end circuitry 116 of fig. 1 b. The digital scan data is then subjected to a clutter filtering process 502 to separate target data from clutter data, which may be a digital filtering process based on the doppler content and amplitude content of the acquired data. Different clutter filtering processes are known in the radar scanning art.

The received and filtered data is then stored, where the first set of data to be stored is scan data for a first image line being scanned, the data representing a difference signal between the transmit signal and the echo signal for each range cell in the first image line, an energy of the echo signal, and an azimuth angle of the first image line position. A dual fourier transform is used on the stored data, thereby generating and storing a range-doppler velocity dataset or map with echo energy for the first image line, step 503. The acquired range-doppler velocity data set or map of the first image line may now be used to initiate generation of a range-doppler velocity map, 504, see figure 6.

The procedure of step 503 is repeated in step 505 for the next image line, where scan data for the next image line being scanned is stored, the data representing the difference signal between the transmit signal and the echo signal for each range cell in the next image line, the energy of the echo signal, and the azimuth angle of the next image line position. A double fourier transform is used for the stored data, thereby generating and storing a range-doppler velocity dataset or map with echo energy for the next image line, and the obtained range-doppler velocity dataset or map for the next image line can now be used for further generation of a range-doppler velocity map, 506.

The procedure of step 505 is repeated for each image line during a full 360 degree scan rotation to acquire data for a full circle radar image, step 507. Each range cell of the complete radar image hit now contains data for: the energy of the echo signal(s), the range to the location of the range cell, the azimuth to the image line location, and one or more radial velocities from the doppler shift(s) and mapped into the range-doppler plot. The range, azimuth and echo energy data of all image lines of the complete radar image are now used for the generation of a range-azimuth graph, 508, see fig. 7.

Fig. 6 is a flow chart illustrating the generation of a first type of radar map or range-doppler velocity map containing range, radial velocity and echo energy information according to an exemplary embodiment. The overall process of figure 6 is referred to as "range-doppler velocity map generation" 600 and begins with a generated range-doppler velocity dataset comprising echo energy data from the first image line output at step 504 of the "map input data generation" 500.

The generation of a first type of radar map or range-doppler velocity map is initiated at step 601, wherein the velocity and range data of the first generated range-doppler velocity data set of the first image line is analyzed. Based on this analysis, neighboring range cells (if any) with matching velocity and range data are grouped into one or more corresponding range-doppler velocity maps. Examples of matching conditions are known in the art of radar scanning and map generation, but a set of corresponding data may satisfy a matching condition when the difference between the matched data is below a predetermined threshold difference. Thus, there may be a maximum threshold difference in the distances used to define the adjacent range cells being matched, and a maximum threshold difference in the radial velocity of these adjacent range cells before the cells in the map are matched. The generated range-doppler velocity map, including the echo energy data, is stored,

steps

602 and 607.

Generation of a first type radar map or range-doppler velocity map is performed at step 604 and is based on the generated range-doppler velocity dataset comprising echo energy data from the next image line output at step 506 of the "map input data generation" 500; while also being based on a previously generated range-doppler velocity dataset comprising echo energy data of the first image line, step 603. The procedure in step 604 is similar to the procedure in step 601, the velocity and range data of the generated range-doppler velocity dataset for the first and next image line are analyzed, and adjacent range pixels with matching velocity and range data are grouped into one or more corresponding range-doppler velocity maps. The generated range-doppler velocity map, including the echo energy data, is stored,

steps

605 and 607. To form a graph, at least two adjacent distance cells with matching data are required, but preferably, to form a graph, at least four, five or six adjacent distance cells with matching data are required.

The procedure of step 604 is repeated for the data sets obtained for the subsequent image lines, step 606, until the data set for each image line of the complete circumferential radar image has been analyzed. Thus, the velocity and range data of the generated range-doppler velocity dataset for a new image line is analyzed together with the received dataset of the previous image line, and adjacent range pixels with matching velocity and range data are grouped into one or more corresponding range-doppler velocity maps. The generated range-doppler velocity map, including the echo energy data, is stored,

steps

605 and 607. The range-doppler velocity map stored at step 607 may be used as an input, step 608, when the maps are combined into a complete data type map. See fig. 9 for a combination of figures.

Fig. 7 is a flow chart illustrating the generation of a second type of radar map or range-azimuth map containing range, azimuth and echo energy information according to an exemplary embodiment, the entire process of fig. 7 is referred to as "range-azimuth map generation" 700 and begins at step 701 with the generated range, azimuth and echo energy data set of the full circle radar image output at step 508 from "map input data generation" 500.

The generation of a second type of radar map or range-azimuth map of the complete radar image is performed at step 702, wherein range and azimuth data for range pixels of each image line within the complete circumferential radar image is analyzed. Based on this analysis, neighboring range cells having matching azimuth and range data are grouped into one or more corresponding range-azimuth maps. Further here, when a difference between matched data is lower than a predetermined threshold difference, a set of corresponding data may satisfy the matching condition. Thus, there may be a maximum threshold difference in the distances used to define the neighboring range cells that are matched, and there may be a maximum threshold difference in the azimuth range of those neighboring range cells before the cells in the graph are matched. The generated range-azimuth map, comprising the echo energy data of the complete circumferential radar image, is stored, step 703. The distance-azimuth map stored at step 703 may be used as input, step 704, when the map is assembled into a complete data type map. See figure 9 for combinations of figures. Furthermore here, at least two adjacent distance cells with matching data are required, whereas preferably, for forming the graph, at least four, five or six adjacent distance cells with matching data are required.

FIG. 8 is a general flowchart illustrating the processing steps performed to achieve classification of a detected object in accordance with an exemplary embodiment. The process diagram of fig. 8 begins with diagram input data generated at process 500. Based on the map input data, a range-doppler velocity map is generated, process 600, and stored, step 607 of process 600. Additionally, a range-azimuth plot is generated, process 700, and stored, step 703 of process 700.

For a full circle radar scan, the range-doppler velocity map of process 700 is generated during the scan, starting when scan data is obtained for the first image line, and first generating the range-azimuth map when a full scan has been performed. When the first set of range-azimuth maps has been generated for the first circular radar scan, and thus for the first complete radar image, the process proceeds by comparing and combining the obtained range-doppler velocity map 608 and range-azimuth map 704 into a complete data type map, step 801, which is further described in connection with fig. 9. It is noted that during a subsequent circular radar scan, a new range-doppler plot is generated and stored during the scan, and when the subsequent circular radar scan is completed, a new range-azimuth plot is first generated and stored. This procedure of map generation is repeated for each complete circumferential radar scan.

When the obtained range-doppler velocity map is compared with the range-azimuth map at step 801, the range-azimuth map that cannot be matched with the range-doppler velocity map is forwarded via step 804 to a so-called "bird tracker" 806. Range-doppler velocity maps that cannot be matched to range-azimuth maps are discarded.

In step 801, the remaining plots are combined into a complete data type plot, which now contains range, azimuth, radial velocity, and echo energy data for the detected objects that form part of the plot. The next step is to partition the complete data type map based on the doppler velocity profile, step 802, which is further described in connection with fig. 10. The combined graph is divided into: a map with an unconventional doppler velocity profile, step 803, and a map with a conventional doppler velocity profile, step 804. The map with the non-conventional doppler velocity profile is fed to a so-called "Unmanned Aerial Vehicle (UAV) tracker", step 805, to generate a so-called "UAV trajectory", step 807, which is further described in connection with fig. 11 and 14. The map with the conventional doppler velocity profile may be fed to a so-called "bird tracker", step 806, to generate a so-called "bird trajectory", step 807, which is further described in connection with fig. 12 and 13. A map with unconventional doppler velocities that does not fit any UAV trajectory 807 can be sent to bird tracker 806 to see if the map fits any bird trajectory 808. The obtained UAV trajectories 807 and bird trajectories 808 may be subjected to a classification process, step 809, to form classified trajectories, step 810. An example of a classification process is further described in conjunction with fig. 15.

FIG. 9 is a flowchart illustrating the generation of a complete data-type radar map containing range, azimuth, radial velocity, and echo energy information in accordance with an illustrative embodiment. The diagram of fig. 9 corresponds to step 801 of fig. 8, where the graphs are combined into a complete data type graph. The received range-doppler velocity map 608 and range-azimuth map 704 contain range data and in step 801a the range data of the range-azimuth map is compared with the range data of the range-doppler velocity map. The maps with matching range data (where there may be a maximum threshold difference in range for defining the matched maps) are combined into a full data type map, step 801c, while in step 801b, the azimuth-range maps without range matching to the range-doppler velocity map are forwarded to the bird tracker and the range-doppler velocity maps without range matching to the range-azimuth maps are discarded. Each composite graph now contains the following data from multiple hit distance cells: the energy, range, azimuth, and one or more radial velocities of the echo signal(s). Furthermore for a combined map, it is preferred that at least four, five or six adjacent distance cells with matching data are required in order to form the map.

The resulting combined map of step 801c may then be divided based on the doppler velocity profile, step 802, as described in connection with fig. 10, which is a flow chart showing the division of the complete type of radar map obtained into a conventional map and an unconventional map based on velocity information. As shown and described in connection with fig. 4a and 4b, for each range cell, the range-doppler matrix contains doppler signatures having a plurality of intervals covering the radial velocity span covered by the scanning radar system 101. The combined plot may represent data from one or more adjacent range cells having one or more corresponding adjacent doppler signatures, and the doppler signatures may represent radial velocities within a span of radial velocities, which may include positive radial velocities and negative radial velocities.

Thus, the first step in dividing the combined map is to analyze the radial or doppler velocity data of the map. This is done in step 802a and if the map contains data representing only positive velocities or only negative velocities, the map is stored as a map with a conventional doppler velocity profile, step 804. If the map contains data representing both positive and negative speeds, it is determined whether the difference between the maximum and minimum speeds, i.e., the difference between the most positive and most negative radial speeds, is greater than or equal to a predetermined minimum speed difference of delta speeds. If the difference between the maximum radial velocity and the minimum radial velocity is below a predetermined minimum velocity difference, the map is saved as a map with a conventional doppler velocity profile, step 804, whereas if the difference between the maximum radial velocity and the minimum radial velocity is equal to or above the predetermined minimum velocity difference, the map is saved as a map with a non-conventional doppler velocity profile, step 803.

The predetermined minimum speed difference between the most positive radial speed and the most negative radial speed may be selected to be at least 50%, such as at least 60%, such as at least 70% or such as at least 75% of the total radial speed span covered by the scanning radar system 101. In an embodiment, the FMCW radar system 101 is configured to provide scanning data indicative of radial velocities in a range of negative 30m/s to positive 30m/s, and here, the predetermined minimum velocity difference between the most positive radial velocity and the most negative radial velocity may be set to at least 45 m/s.

FIG. 11 is a general flow diagram illustrating the generation of Unmanned Aerial Vehicle (UAV) trajectories according to an exemplary embodiment. The illustration of fig. 11 corresponds to the UAV tracker of fig. 8, step 805. The tracking procedure 805 is based on the map with the non-conventional doppler velocity profile established in step 803. The first step is to analyze whether the graph matches any existing UAV trajectory, step 805 a. If no trajectory has been generated, or if there is no match, the map is stored and can be used to generate a new UAV trajectory, step 805 b. If the graph becomes too old to match other graphs to form a trace, the stored graph may be discarded and may be fed into a bird tracker, step 806. If there are several stored maps with matches, such as at least two or three, but preferably at least four, five or six matched maps, a new UAV trajectory may be generated, step 805c, and stored as a UAV trajectory, step 807. When the new map is matched to existing UAV trajectories, the stored UAV trajectories are used in step 805a and if there is a match, the matching UAV trajectories are updated with the data of the new map, step 805 d. The generated and stored UAV trajectories 807 can then be classified, step 809, as further described in connection with fig. 15. To form a map at least two adjacent distance cells with matching data are required, but preferably at least 4, 5 or 6 adjacent distance cells with matching data are required to form a map.

FIG. 12 is a general flow diagram illustrating the generation of bird trajectories according to an exemplary embodiment. The illustration of FIG. 12 corresponds to the bird tracker of FIG. 8, step 806. The tracking procedure 806 is based on the map with the conventional doppler velocity profile from step 804 and the map without the doppler velocity profile from

steps

801 and 804, but may also include a map discarded from the UAV tracker, step 805 b. The first step is to analyze whether the graph matches any existing bird trajectory, step 806 a. If no trajectories have been generated, or if there is no match, the graph is stored and can be used to generate new bird trajectories, step 806 b. If the graph becomes too old to match with other graphs to form a trace, the stored graph may be discarded. Checking in step 806e whether the discarded drawing is a discarded UAV drawing; if not, the graph is eventually discarded or discarded, step 806f, and if so, the graph may be forwarded to a classifier 809. If there are several stored graphs with matches, such as at least two or three, but preferably at least four, five or six matched graphs, a new bird trajectory may be generated, step 806c, and stored as a bird trajectory, step 808. When the new graph is matched to existing bird tracks, the stored bird tracks are used in step 806a, and if there is a match, the matching bird tracks are updated with the data of the new graph, step 806 d. The bird trajectories 808 generated and stored may then be classified, step 809, as further described in connection with FIG. 15.

Examples of matching conditions are known in the field of radar scanning and trajectory generation, but when a new trajectory is generated based on multiple graphs, a set of data that is part of the graphs being compared may have to satisfy the matching conditions, whereas when a new graph is used to update an existing trajectory, the data of the new graph has to match the corresponding data of the stored trajectory. When the difference between the matched data is below a predetermined threshold difference, the corresponding map or trajectory data may satisfy the matching condition. This is further described in conjunction with fig. 13 for bird trajectories and fig. 14 for UAV trajectories.

FIG. 13 is a detailed flow chart illustrating the generation of bird trajectories. The diagram of FIG. 13 corresponds to the bird tracker of FIG. 8, step 806, and is a more detailed example of the bird tracking process of FIG. 12. Thus, the tracking procedure 806 is based on the map with the conventional doppler velocity profile and/or the map without the doppler velocity profile established in step 804, and may also include the map discarded from the UAV tracker, step 805 b. The first step is to analyze whether the graph matches any existing bird trajectory, step 806 a. If there are any existing bird trajectories, then multiple data must match: the location of the tracked object must match the location of the object of the new map, step 806aa, where the location may be based on the distance and azimuth data; the direction of movement of the tracked object must match the direction of movement of the object of the new map, step 806ab, where the direction of movement of the trajectory is based on the positions of several maps and the direction of movement of the new map means that the position of the new map should match the direction of movement given in the trajectory; the velocity and doppler radial velocity of the tracked object must match the velocity and doppler radial velocity of the object of the new map, step 806ac, where the velocity of the trajectory is based on the change in position of the several maps, and the velocity of the new map means that the position of the new map should match the velocity and direction of motion given in the trajectory; the size of the tracked object must match the size of the object of the new map, step 806ad, where the object size may be based on the sum of the echo signal energies within the map. If the new graph satisfies all matching steps, the matching bird trajectories are updated with the data of the new graph, step 806 d.

If no trajectories have been generated, or if there is no match, a new graph is stored and can be used to generate new bird trajectories, step 806 b. If the graph becomes too old, i.e., earlier than a given maximum time T, to match with other graphs to form a trace, the stored graph may be discarded. Checking in step 806e whether the discarded drawing is a discarded UAV drawing; if not, the graph is eventually discarded or discarded, step 806f, and if so, the graph may be forwarded to a classifier 809. When a number of stored graphs that are not too old have been obtained, the graphs are checked to see if the number of matching conditions can be met to generate a new trajectory. The matching conditions for the graph used to form the new trajectory may be similar to the matching conditions for the new graph and the existing trajectory: the positions of the objects of the map must match each other, step 806ba, where the positions may be based on distance and azimuth data; the moving direction of the objects of the graphs must match, step 806bb, where the moving direction is based on the position of the objects of the several graphs; the velocity of the object of the map and the doppler radial velocity must match, step 806bc, where the velocity of the object is based on the change in object position of the several maps; the size of the objects of the map must match, step 806bd, where the object size may be based on the sum of the energies of the echo signals within the map. If there are several stored graphs with matches, such as at least two or three, but preferably at least four, five or six matched graphs, a new bird trajectory may be generated, step 806c, and stored as a bird trajectory, step 808. When the new graph is matched to existing bird trajectories, the stored bird trajectories are used in step 806 a. The bird trajectories 808 generated and stored may then be classified, step 809, as further described in connection with FIG. 15.

Fig. 14 is a detailed flow chart illustrating the generation of Unmanned Aerial Vehicle (UAV) trajectories. The illustration of fig. 14 corresponds to the UAV tracker of fig. 8, step 805, and is a more detailed example of the UAV tracking process of fig. 11. Therefore, the tracking procedure 805 is based on the map with the non-conventional doppler velocity profile established in step 803.

The first step is to analyze whether the graph matches any existing UAV trajectory, step 805 a. If there are any existing UAV trajectories, then multiple data must match: the doppler radial velocity of the tracked object must match the doppler radial velocity of the object of the new map, step 805 aa; the location of the tracked object must match the location of the new map object, step 805ab, where the location may be based on distance and azimuth data; the size of the tracked object must match the size of the object of the new map, step 805ac, where the object size may be based on the sum of the echo signal energies within the map. If the new map satisfies all matching steps, the matching UAV trajectory is updated with the data of the new map, step 805 ad.

If no trajectory has been generated, or if there is no match, a new map is stored and can be used to generate a new UAV trajectory, step 805 b. If the graph becomes too old, i.e., earlier than a given maximum time T, to match with other graphs to form a trace, the stored graph may be discarded, step 805 bd. The discarded UAV map may then be sent to a bird tracker, step 806, to see if the map fits to bird trajectories. When a number of stored graphs that are not too old have been obtained, the graphs are checked to see if the number of matching conditions can be met to generate a new trajectory. The matching conditions for the graph used to form the new trajectory may be similar to the matching conditions for the new graph and the existing trajectory: the doppler radial velocities of the objects of the map must match, step 805 ba; the positions of the objects of the map must match, step 805bb, where the positions may be based on distance and azimuth data; the size of the objects of the map must match, step 805bc, where the object size may be based on the sum of the echo signal energies within the map. If there are several stored maps with matches, for example at least two or three, but preferably at least four, five or six matched maps, a new UAV trajectory may be generated, step 805c, and stored as a UAV trajectory, step 807. When matching the new map to existing UAV trajectories, the stored UAV trajectories are used in step 805 a. The generated and stored UAV trajectories 807 can then be classified, step 809, as further described in connection with fig. 15 and 16.

An important aspect of the present invention is to provide data that can be used to track and classify UAVs or UAV-like objects. Thus, for the systems and methods of the present disclosure, tracking of birds or similar avian subjects and classification of birds or similar avian subjects may be optional. Notably, all objects tracked by the UAV trajectory have positive and negative radial or doppler velocities, which may indicate, for example, that the tracked object has one or more propeller blades; however, flying objects with propeller blades may be of widely varying sizes, where an unmanned UAV may be of a fairly small size and have low energy or amplitude in the return radar signal, while a manned helicopter will have much higher energy or amplitude in the return radar signal.

For an object tracked by the UAV trajectory, the received reflected energy represents a radial velocity span from an external negative velocity to an external positive velocity, where a majority of the reflected energy represents a radial velocity near the center of the velocity span. This is shown in fig. 16, which contains an energy curve 1600 showing reflected energy in dB as a function of radial velocity or doppler velocity. The curve 1600 is based on the sum of the echo energies from range cells or bins of doppler signatures forming a plot of UAV trajectories with echo energy data for different radial/doppler velocities. The curve 1600 has a central portion 1601 representing the highest amount of energy, which for this embodiment is centered around a slightly positive doppler velocity. The central portion 1601 represents the echo energy, which may correspond to a central body of the UAV. Curve 1601 also has a negative external energy portion 1602 and a positive external energy portion 1603. The

external energy portions

1602 and 1603 represent echo energy within a so-called micro-doppler distance and may correspond to energy reflected from UAV propeller blades.

For curve 1601, the boundary between the central portion 1601 and the

external energy portions

1602 and 1603 is set to limit

speeds

1604 and 1605. For the embodiment shown in fig. 16, the energy level of the central portion 1601 decreases to a minimum on both sides of the maximum energy velocity, and then the energy starts to increase slowly again. The limit speeds 1604, 1605 are selected to be speeds at which the energy increases by 3dB from the minimum energy.

For flying objects such as UAVs, the micro-doppler signal must have a certain strength compared to the strength of the central body signal in order for the propeller blades to keep the UAV flying. In fig. 16, the intensity of the micro-doppler signal is represented by the sum of the energies in the

outer energy portions

1602 and 1603, and the intensity of the center body signal is represented by the sum of the energies in the center portion 1601. Thus, to classify the object of the UAV trajectory as a UAV, the strength of the external doppler velocity signal should be higher than some or a predetermined fraction of the strength of the central doppler velocity signal. Here, the intensity of the outer doppler velocity signal may be not less than 1/1000 of the intensity of the center doppler velocity signal, or the intensity of the outer doppler velocity signal should not be weaker than the intensity of the center doppler velocity signal by more than 30 dB.

As mentioned above, the flying objects of the UAV trajectory may vary in size, where the manned helicopter will have much higher energy or amplitude in the return radar signal. Thus, if the total strength of the received doppler velocity signals, represented by the sum of the echo energies of all doppler velocity signals (which for the curve 1600 of fig. 16 would be the sum of the energy of the central portion 1601 and the energy of the outer portions 1602 and 1603), is above some or a predetermined maximum, the object trajectory may be classified as a non-UAV trajectory. Here, if the sum of the echo energies indicates a sum with energy higher than 1m²Of radar cross section (RSC), the trajectory may be classified as a non-UAV trajectory.

FIG. 15 is a flowchart illustrating classification of an obtained trajectory according to an exemplary embodiment. Fig. 15 shows classification 809 of detected objects as part of UAV trajectory 807 and detected objects as part of bird trajectory 808, where classification of birds or similar bird objects may be optional. In classifying the UAV trajectory, a first step, step 809a, may be to determine the sum of the echo energies of range cells representing doppler velocities within the center range of the observed velocity range and to determine the sum of the echo energies of range cells representing positive and negative doppler velocities within the outer range of the observed velocity range. Step 809a may also include determining a sum of echo energies for range cells for all doppler velocity signals, the sum of echo energies representing a total body weight with corresponding radar scatter cross-sections.

The next step, step 809b, is to determine if the aggregate external velocity energy is above a predetermined fraction of the aggregate central velocity energy. Alternatively, it may be determined whether the aggregate external speed energy is above a predetermined fraction of the aggregate speed energy. If not, the trajectory is classified as a non-UAV trajectory, step 809d, and if so, the trajectory may be a UAV trajectory. However, in step 809b, it is also determined whether the summed velocity energy is less than or equal to the aggregate energy representing the total body weight with the predetermined maximum radar scattering cross section. If not, the trajectory is classified as a non-UAV trajectory, step 809d, and if yes, if the requirement for aggregate external speed energy is also yes, the trajectory is classified as a UAV trajectory.

For this embodiment, the traces are classified by the computer system 102 and the classified traces are forwarded from the computer system to the command and control system 103 by real-time streaming, where the classified traces may be displayed in real-time, 810a, and then stored in a database, step 810 b.

Thus, an important result of the radar scanning process described herein is to display the generated and classified UAV trajectories in real-time at the command and control system 103. Based on the information of the displayed trajectory, the personnel of the command and control center may decide whether action needs to be taken for the detected UAV.

The main difference between radar-based scanning of birds and UAVs is that a large number of bird maps, here conventional doppler velocity profiles, such as up to 1000 to 5000 maps, may be generated, while only a very few UAV maps, here unconventional doppler velocity profiles, such as two, four or six maps, may be generated. Thus, only a single or few UAV objects may be tracked, while more birds or bird groups may have to be tracked and distinguished, whereby bird tracking and classification needs to be more accurate and thus based on a higher number of variables than UAV tracking and classification, where radial or doppler velocity profiles give the most important information for UAV tracking.

Thus, the first step in the bird trajectory classification process may be to determine other parameters based on the obtained data of the trajectory, as shown in step 809 aa. The total object mass and the distribution of the object masses are determined from the energy of the echo signal(s) from the map data, wherein the mass distribution can be determined on the basis of the echo energy data of the interval of the doppler characteristic signals of the map. Further, the speed, direction, acceleration and curvature of the motion profile may be determined from the object trajectory data.

Based on the results obtained from the trajectory and corresponding map determination, the trajectory may be classified as a bird trajectory or a non-bird trajectory, step 809 cc. Here, the non-avian object may be an aircraft, which has a much larger size and object mass, and which has a higher speed and better bending than birds or a flock of birds. For a trajectory classified as a bird trajectory, the trajectory may be classified as a small bird trajectory, a medium bird trajectory, a large bird trajectory, or a flock bird trajectory based on results determined from the graph and trajectory data. Here, size or object mass may be used to classify birds as small, medium or large, and curvature or lack of curvature may be good parameters to classify objects as a group of birds. For systems that include bird trajectories and classifications, the classified bird trajectories may be forwarded to the command and control system 103 by real-time streaming, where the classified trajectories may be displayed in real-time, 810a, and then stored in a database, step 810 b.

Notably, the discarded UAV plot, which does not match any bird trajectory, 806e, may be classified as a self trajectory, which may be a so-called "hovering UAV trajectory," which may also be forwarded to the command and control system 103 by real-time streaming, where the classified trajectory may be displayed in real-time, 810a, and then stored in a database, step 810 b.

The FMCW radar scanning system for detecting an object, and the program or method for generating a radar map and a trajectory based on the obtained scan data have been discussed in connection with the foregoing description of fig. 1 to 6. The generation of the trajectory includes generation of a trajectory that conforms to an Unmanned Aerial Vehicle (UAV), and procedures for classifying the generated UAV trajectory as a true UAV trajectory are also described.

However, within embodiments of the present disclosure, a system is provided that is capable of detecting and tracking UAVs, and that is also capable of distinguishing between known and unknown UAVs. Such a system may also be configured to control the operation of a known UAV based on obtained tracking information of an unknown UAV. Such a system is illustrated in fig. 17, which is a schematic block diagram illustrating the basic structure of an Unmanned Aerial Vehicle (UAV) system in accordance with an exemplary embodiment.

The system of fig. 17 comprises an FMCW scanning radar system 101 electronically connected to a computer system 102, which are equivalent to the radar and

computer systems

101 and 102 of fig. 1 a. The generated output data from the computer system 102 is transmitted to an external command and control system or station 103, which is also shown in FIG. 1 a. The system of fig. 17 also includes a known first cooperative Unmanned Aerial Vehicle (UAV)1701 and an antenna 1705. The cooperative UAV1701 is equipped with a telemetry transmitter/receiver 1702 for exchanging telemetry Radio Frequency (RF) data with an antenna 1705, which is also in data communication with the command and control station 103 so that the flight path of the cooperative UAV1701 may be controlled from the command and control station 103. The cooperative UAV1701 also contains a camera 1704, such as a video camera, so that real-time video signals can be sent from the UAV1701 via an antenna 1705 to the command and control station 103. The cooperative UAV1701 may also be equipped with a transponder 1703 containing identification Information (ID) of the UAV1701 so that transponder data containing the identification information may be forwarded from the UAV1701 to the command and control station 103. The cooperative UAV1701 may also contain a Global Positioning System (GPS), which is not shown in fig. 17, and the telemetry data forwarded to the command and control station 103 may contain location data obtained from the GPS system.

In fig. 17, the cooperative UAV1701 is exposed to radar signals from the radar system 101 and is thereby detected by the radar system 101. Fig. 17 also shows a second Unmanned Aerial Vehicle (UAV)105, which in this case is an unknown or uncooperative UAV105 that is not in telemetric communication with the command and control system 103. Thus, radar scan data is obtained by the radar system 101 for the first UAV1701 and the second UAV105, which scan data may be processed by processing circuitry of the computer system 102 to obtain radar maps and object trajectories of the detected

UAVs

105 and 1701. Further, the command and control system 103 receives telemetry data and possibly transponder data from the first UAV1701, which may be forwarded to the computer system 102 and compared to the obtained UAV trajectory data. The end result of such a comparison may be a classification of the obtained UAV trajectories, distinguishing between trajectories of known cooperating UAVs and trajectories of unknown non-cooperating UAVs. The classified UAV trajectory may be forwarded and displayed in real-time at the command and control system 103.

Based on the information of the displayed trajectory, it is possible for personnel at the command and control central station 103 to decide whether action needs to be taken for the detected UAV, and may forward the flight path instructions and possibly action instructions via telemetry communications to the known cooperative UAV1701, which may adjust its flight path accordingly and execute any received action instructions accordingly.

The formation and classification of UAV trajectories for known and unknown UAVs is illustrated in fig. 18 and 19, where fig. 18 is a flow chart illustrating the process steps including map generation, trajectory generation, and classification of detected objects based on data obtained by the system of fig. 17 according to an exemplary embodiment. The first part of the steps of fig. 18 follows the first part of the steps of fig. 8, which also refers to the generation of graph input data from fig. 5, step 500; generation of the range-doppler velocity map from fig. 6,

steps

600 and 607; and generation of the distance-azimuth graph from fig. 7,

steps

700 and 703.

From the obtained range-doppler velocity map 703 and the obtained range-azimuth map 703, the process proceeds by comparing and combining the obtained

maps

608 and 704 into a complete data type map, step 801, which is further explained in connection with fig. 9. It is noted that during a subsequent circular radar scan, a new range-doppler plot is generated and stored during the scan, and when the subsequent circular radar scan is completed, a new range-azimuth plot is first generated and stored. The procedure of map generation is repeated for each complete circumferential radar scan.

When comparing the obtained range-doppler velocity map and range-azimuth map at step 801 in fig. 8, the range-azimuth map that cannot be matched to the range-doppler velocity map is forwarded via step 804 to a so-called "bird tracker" 804. The range-doppler velocity map that cannot be matched to the range-azimuth map is discarded. However, for the processing step of fig. 18, we only need to look at the map that is consistent with generating the UAV trajectory, so for step 801 the range-azimuth map that cannot be matched to the range-doppler velocity map can be forwarded to the "bird tracker" or can be discarded along with the range-doppler velocity map that cannot be matched to the range-azimuth map.

The remaining plots in step 801 are combined into a complete data type plot, which now contains range, azimuth, radial velocity, and echo energy data for the detected objects that form part of the plot. The next step is to select a generated complete data type map that conforms to the UAV trajectory, and the selection is based on the doppler velocity profile, step 802, which is further described in connection with fig. 10. The generated combined map selected to conform to the UAV trajectory is a map with an unconventional doppler velocity profile, step 803, where the unconventional type radar map has velocity data representing positive and negative radial velocities within the observed range of radial velocities. The selected unconventional type of radar map may have velocity data representing positive and negative radial velocities with at least one predetermined minimum velocity difference between the most positive and most negative radial velocities.

The map with the non-conventional doppler velocity profile is fed to a so-called "Unmanned Aerial Vehicle (UAV) tracker", step 1805, to generate a so-called "UAV trajectory", step 1807, which is further described in connection with fig. 19. In step 1805, a UAV trajectory is formed based on the at least two matched graphs having the non-conventional doppler distribution. However, the UAV tracker, step 1805, also receives telemetry data (if any), step 1812, and transponder data (if any), step 1813, which may be derived from a known UAV. Any received UAV telemetry data and transponder data is stored with the UAV trajectory data, step 1807.

The obtained UAV trajectory 1807 now undergoes a classification process, step 1809, to get a classified real UAV trajectory. An example of a classification process that may be used in step 1809 to classify the UAV trajectory as a real UAV trajectory is described in connection with fig. 15. The UAV trajectory classified as a real UAV trajectory is further classified as representing a first known or cooperating UAV, step 1810, or representing a second unknown or non-cooperating UAV, step 1811. A real UAV trajectory having a match between the corresponding trajectory data and the received telemetry data and/or transponder data is classified as a cooperative UAV trajectory, step 1810, and a real UAV trajectory having no match between the corresponding trajectory data and the received telemetry data and/or transponder data is classified as a non-cooperative UAV trajectory, step 1811.

Fig. 19 is a general flow diagram illustrating the generation of an Unmanned Aerial Vehicle (UAV) trajectory based on data obtained by the system of fig. 17, according to an exemplary embodiment. The illustration of fig. 19 corresponds to the UAV tracker 1805 of fig. 18 and may include the same steps described in connection with the UAV tracker 805 of fig. 11. The main difference is that UAV tracker 1805 of fig. 18 and 19 also receives telemetry data (if any), step 1812; and transponder data (if any), step 1813, the telemetry data and transponder data may be derived from a known UAV.

Thus, the tracking procedure 1805 is based on the map with the non-conventional doppler velocity profile established in step 803. The first step is to analyze if the map matches any existing UAV trajectory, step 1805 a. If no trajectory has been generated, or if there is no match, the map is stored and can be used to generate a new UAV trajectory, step 1805 b. If the graph becomes too old to match other graphs to form a trace, the stored graph may be discarded or may be fed into a bird tracker, step 806. If there are several stored maps with matches, such as at least two or three, but preferably at least four, five or six matched maps, a new UAV trajectory may be generated, step 1805c, and stored as a UAV trajectory, step 1807. When the new map is matched to existing UAV trajectories, the stored UAV trajectories are used in step 1805a and if there is a match, the matching UAV trajectories are updated with the data of the new map, step 1805 d. Further in

steps

1805a and 1805d, the received telemetry data and/or transponder data is matched with the data of the generated UAV trajectory, which is then stored with the data of the matched trajectory at step 1807. The generated and stored UAV trajectory 1807 may then be classified as a real or non-real UAV trajectory, step 1809, and further classified as: representing a first known or cooperating UAV, step 1810 of fig. 18; or a second unknown or uncooperative UAV, step 1811 of fig. 18.

When matching a graph with an unconventional doppler velocity profile to generate UAV trajectory 1805c, or matching a graph to an existing UAV trajectory 1805a, the matching may include determining if there is a match between the radial velocity data, the range data, and the echo energy data. The matching of the echo energy data of the two unconventional type radar maps may be based on a sum of echo energies of all radial velocity signals representing each of the two unconventional type radar maps being matched and/or on a sum of central echo energies corresponding to a central radial velocity span within the observed radial velocity range of each of the two unconventional type radar maps being matched. If the difference between the data is equal to or below a predetermined threshold difference, a corresponding set of data may satisfy the matching condition.

When matching the received telemetry data and/or transponder data with the data of the generated UAV trajectory,

steps

1805a and 1805d, it may be necessary to satisfy a predetermined matching condition between the stored data of the trajectory and the received telemetry data and/or transponder data. Here, the matching condition may include a match between position data, a match between velocity data, and/or a match between size data. Also here, if the difference between the data is equal to or less than a predetermined threshold difference, a set of corresponding data may satisfy the matching condition.

FIG. 20 is a block diagram illustrating the exchange of signals between portions of the system of FIG. 17, according to an exemplary embodiment. The radar system 101 provides scan data that is processed by the computer system 102 into UAV trajectories. The instruction and control station 103 receives telemetry data and/or transponder data from the first cooperating UAV, which may be further forwarded to the computer system 102, which then classifies the obtained UAV trajectories into cooperating UAV trajectories and non-cooperating UAV trajectories, step 2001. The trajectory data of the classified trajectory is then forwarded to the command and control station 103.

The command and control station 103 determines from the received trajectory data whether there are any second uncooperative UAV trajectories, step 2002. If so, the command and control station 103 begins generating and forwarding flight plan commands to the first cooperative UAV1701, step 2007, based on object data from the object trajectory of the second uncooperative UAV105 and based on telemetry data and/or transponder data received from the first cooperative UAV 1701. The first cooperative UAV1701 receives flight path instructions, as shown at step 2008.

The first cooperative UAV1701 contains a camera 1704 and the telemetry data received by the command and control station 103 includes video signals. Based on the received telemetry data, the command and control station 103 determines whether the second non-cooperative UAV105 is in a video scene of the first cooperative UAV1701, step 2004. If so, the command and control station 103 begins generating and forwarding flight plan commands to the first cooperative UAV1701 based on the received telemetry video signals, step 2005.

In an embodiment, the command and control station 103 is configured to: flight plan instructions are generated and forwarded based on position data obtained from the object trajectory of the second non-cooperative UAV105, step 2003, until the second non-cooperative UAV105 is detected within the received video signal, step 2005. At this point in time, the command and control station 103 may be configured to generate and forward flight plan commands based solely on the received video signals.

When the UAV of the UAV object trajectory is classified as a second non-cooperative UAV105, in an embodiment, the instruction and control circuitry 103 is configured to generate and forward motion instructions to the first UAV1701 based on information obtained from the object trajectory of the second UAV105, steps 2006 and 2009. The action instructions may also be based at least in part on received telemetry data and/or transponder data of the first cooperative UAV 1701. The first cooperative UAV1701 may then be configured to perform an action based on the action instructions received from the command and control station 103, step 2010. The performance of the action may also be based on the received flight path instructions.

According to an embodiment, the action and flight path instructions may include flight interference action instructions whereby the first cooperative UAV1701 is configured to perform a flight route or flight plan interference action for the second non-cooperative UAV 105. Here, the flight route or flight plan interfering action to be performed may be to steer the first cooperative UAV1701 to the second non-cooperative UAV105 to cause a collision between the first and

second UAVs

1701, 105.

The command and control station 103 may generate flight plan information and/or action information based on position, velocity, and/or size data obtained from the object trajectory of the second non-cooperative UAV 105. Here, the position data may include a distance and an azimuth angle with respect to the radar system, and the velocity data may include a radial velocity with respect to the radar system. The sizing data may be determined based on the energy of the echographic signals within the graph forming the object trajectory of the second uncooperative UAV 105.

The invention has been described in connection with various embodiments herein. However, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality.

Claims

1. A frequency modulated continuous wave, FMCW, radar system (101), the system comprising:

one or more antennas (110, 111) configured to transmit and receive FMCW radar wave signals (300, 205) for scanning an object (105) within a full circle detection coverage; and

processing circuitry (116, 102) configured to: providing scan data based on the transmitted and received FMCW radar signals (300, 205) and the azimuth position of the one or more antennas (110, 111), and generating a radar map based on the obtained scan data (801 c); wherein

The processing circuitry (116, 102) is configured to:

providing scan data (504, 506, 508) representing range cells (203) within image lines (201) of a circumferential radar image (200), wherein each radar image (200) comprises a plurality of image lines (201) defining a complete circumferential radar image, each image line corresponding to an azimuthal orientation (204), and wherein each image line (201) comprises a plurality of range cells (203), each range cell corresponding to a range to one or more radar antennas (110, 111), and wherein an object (105) detected within azimuthal orientation and range with respect to one or more radar antennas (110, 111) is represented by a plurality of hit range cells (203) in one or more image lines (201), and wherein each hit range cell (203) comprises data based on one or more radial velocities of a doppler frequency signal and energy of one or more echo signals, whereby the scan data (504, 506, 508) contains, for each hit range cell, information of range, azimuthal orientation, energy of returned radar wave signals, and one or more radial velocities of the detected object; the method is characterized in that:

the processing circuitry (116, 102) is further configured to:

generating first type radar maps (607) of the detected objects based on the obtained scan data (504, 506), wherein each first type radar map is based on data from a plurality of adjacent hit range cells (203) within one or more image lines (201) of a first full circle radar image (200), each said first type radar map (607) containing range, radial velocity and echo energy data of one or more detected objects;

generating second type radar maps (703) of the detected objects based on the obtained scan data (508), wherein each second type radar map is based on data from a plurality of adjacent hit range cells (203) within one or more image lines (201) of the first full circle radar image (200), each second type radar map (703) containing azimuth, range and return energy data of one or more detected objects; and

generating a complete data-type radar map (801c) by combining a first and a second type of radar maps having corresponding range data, whereby each said complete data-type radar map (801c) contains azimuth, range, radial velocity and echo energy data of one or more detected objects.

2. The FMCW radar system of claim 1, wherein the processing circuitry (116, 102) is configured to:

generating a first type radar map (607) based on grouping neighboring hit range cells (203) within one or more image lines (201) of the first full circle radar image (200) having matching range and radial velocity data; and

generating a second type of radar map (703) based on grouping neighboring hit range cells (203) within one or more image lines (201) of the first full circle radar image (200) having matching range and azimuth data.

3. The FMCW radar system of claim 1 or 2, wherein the processing circuitry (116, 102) is configured to:

-initiating the generation of the first type radar map (607) before initiating the generation of the second type radar map (703).

4. The FMCW radar system of any of claims 1-3, wherein the processing circuitry (116, 102) is configured to:

initiating generation of the first type radar map (607) upon obtaining scan data (504) of range cells (203) of a first image line (201) of the first full circle radar image (200);

generating the first type radar map (607) by:

if the hit distance unit (203) is present in the first image line (200), analyzing the distance and radial velocity data obtained for the hit distance unit of the first image line, and

if there is a match, the neighboring hit range units (203) with matching range and radial velocity data are grouped into a plurality of corresponding first type range radar maps (607).

5. The FMCW radar system of claim 4, wherein the processing circuitry (116, 102) is configured to:

continuing the generation of the first type radar map (607) while obtaining scan data (506) for range cells (203) of a next image line (201) of the first full circle radar image (200); and

continuing the generation of the first type radar map (607) until scan data (506) has been obtained for all image lines (201) of the first complete circumferential radar image (200), thereby obtaining first type radar maps (607) of the first complete circumferential radar image (200), each of the first type radar maps (607) containing range, radial velocity and return energy data of one or more detected objects.

6. The FMCW radar system of claim 4 or 5, wherein the processing circuitry (116, 102) is configured to:

initiating generation of the second type of radar map (703) when scan data (508) for all image lines (201) within the first full circle radar image (200) has been obtained; and

generating the second type of radar map (703) by: -analyzing range and azimuth data obtained by the range-hitting units (203) for an image line (201) of the first complete circular radar image (200), and-grouping neighboring range-hitting units (203) with matching range and azimuth data into a plurality of corresponding second type range radar maps (703), thereby obtaining a second type radar map (703) of the first complete circular radar image (200), each second type radar map (703) containing azimuth, range and echo energy data of one or more detected objects.

7. The FMCW radar system of claims 5 and 6, wherein the processing circuitry (116, 102) is configured to:

generating the complete data type radar map (801c) of the first complete circumferential radar image by: comparing range data of the obtained first type of radar map (607) and second type of radar map (703) of the complete circumferential radar image (200), and combining the first type of radar map (607) and second type of radar map (703) with matching range data into corresponding complete data type radar maps (801c), whereby each of the complete data type radar maps (801c) contains azimuth, range, velocity and echo energy data of one or more detected objects.

8. The FMCW radar system of any of claims 1-7, wherein the processing circuitry (116, 102) is configured to:

selecting an unconventional type radar map (803) from a complete data type radar map (801c) with velocity data representing positive and negative radial velocities; or

Selecting a complete data type radar map (801c) as an unconventional type radar map (803) when the speed data of the hit range unit (203) represented by the complete data type radar map (801c) represents positive and negative radial speeds with at least one predetermined minimum speed difference between an observed radial speed having a maximum positive value and an observed radial speed having a negative value with a maximum absolute value.

9. The FMCW radar system of claim 8, wherein the processing circuitry (116, 102) is configured to:

generating one or more Unmanned Aerial Vehicle (UAV) trajectories (807), wherein each UAV trajectory (807) is based on at least two unconventional type radar maps (803) with a match between corresponding data of the at least two unconventional type radar maps.

10. A method of generating a radar map (801c) comprising radial velocity data, the method using a frequency modulated continuous wave, FMCW, radar system (101), the system comprising one or more antennas (110, 111) configured to transmit and receive FMCW radar wave signals for scanning a complete circumference to detect an object (105) within coverage, the system further comprising processing circuitry (116, 102) configured to obtain scan data (504, 506, 508) based on the transmitted and received FMCW radar signals (300, 205) and an azimuthal position of the one or more antennas (110, 111), and to generate a radar map (801c) based on the obtained scan data (504, 506, 508); wherein the method comprises the following steps:

obtaining scan data (504, 506, 508) representing range cells (203) within image lines (201) of a circumferential radar image (200), wherein each radar image (200) comprises a plurality of image lines (201) defining a complete circumferential radar image (200), each image line (201) corresponding to an azimuthal orientation (204), and wherein each image line (201) comprises a plurality of range cells (203), each range cell corresponding to a range to one or more radar antennas (110, 111), and wherein an object (105) detected within azimuthal orientation and range relative to one or more radar antennas (110, 111) is represented by a plurality of range cells (203) of the one or more image lines (201), and wherein each range cell (203) comprises data based on one or more radial velocities of a doppler frequency signal and data of an energy of one or more echo signals, whereby the scan data (504, 506, 508) contains, for each hit range cell, range, azimuth orientation of the detected object, energy of the returned radar wave signal, and information of one or more radial velocities or velocities; characterized in that the method further comprises:

generating first type radar maps (607) of the detected objects based on the obtained scan data (505, 506), wherein each first type radar map (607) is based on data from a plurality of adjacent hit range cells (203) within one or more image lines (201) of a first full circle radar image (200), each said first type radar map (607) containing range, radial velocity and echo energy data of one or more detected objects;

generating second type radar maps (703) of the detected objects based on the obtained scan data (508), wherein each second type radar map (703) is based on data from a plurality of adjacent hit range cells (203) within one or more image lines (201) of the first full circle radar image (200), each second type radar map (607) containing azimuth, range and echo energy data of one or more detected objects; and

generating a complete data type radar map (801c) by combining a first type radar map and a second type radar map with corresponding range data, whereby each said complete data type radar map (801c) contains azimuth, range, radial velocity and echo energy data of one or more detected objects.

11. The method of claim 10, wherein,

the generation of the first type of radar map (607) is based on grouping neighboring hit range cells (203) within one or more image lines (201) of the first full circle radar image (200) with matching range and radial velocity data, and

the generation of the second type of radar map (703) is based on grouping neighboring hit range cells (203) within one or more image lines (201) of the first full circle radar image (200) with matching range and azimuth data.

12. The method of claim 10 or 11, wherein the generation of the first type of radar map (607) is initiated before the generation of the second type of radar map (703) is initiated.

13. The method according to any one of claims 10 to 12, wherein the generation of the first type radar map (607) is initiated when obtaining scan data of range cells (203) of a first image line (201) of the first full circle radar image (200).

14. The method of claim 13, wherein generating the first type radar map (607) comprises:

15. The method of claim 13 or 14, wherein the step (607) of generating the first type of radar map further comprises:

16. The method of claim 15, wherein the continuous generation of the first type radar map (607) is performed by:

analyzing the distance and radial velocity data obtained by the hit distance unit (203) for different image lines (201), an

Adjacent hit range cells (203) having matching range and radial velocity data are grouped into a plurality of corresponding first type range radar maps (607).

17. The method according to any one of claims 13 to 16, wherein the generation of the second type radar map (703) is initiated when scan data (508) for all image lines (201) within the first full circle radar image (200) has been obtained.

18. The method of claim 15, wherein the generating of the second type of radar map (703) comprises:

analyzing range and azimuth data obtained by the hit range unit (203) for an image line (201) of the first full circle radar image (200), and

-grouping neighboring hit range units (203) with matching range and azimuth data into a plurality of corresponding second type range radar maps (703), thereby obtaining second type radar maps (703) of the first full circle radar image (200), each second type radar map (703) containing azimuth, range and return energy data of one or more detected objects.

19. The method of claim 15 or 16 and 18, wherein the generation of the complete data type radar map (801c) of the first complete circumferential radar image (200) is performed by:

comparing the distance data of the first type of radar map (607) and the second type of radar map (703) obtained of the complete circumferential radar image (200), and

combining the first type of radar map (607) and the second type of radar map (703) with matching range data into corresponding complete data type radar maps (801c), whereby each of said complete data type radar maps (801c) contains azimuth, range, velocity and return energy data of one or more detected objects.

20. A frequency modulated continuous wave, FMCW, radar system (101), the system comprising:

one or more antennas (110, 111) configured to transmit and receive FMCW radar wave signals (205, 300) for scanning an object (105) within full circle detection coverage, such as an Unmanned Aerial Vehicle (UAV); and

processing circuitry (116, 102) configured to:

providing scan data (501) based on the transmitted and received FMCW radar signals (205, 300) and the azimuthal position of the one or more antennas (110, 111), the scan data (501) representing range cells (203) within image lines (201) of a circumferential radar image (200), wherein each radar image (200) contains a plurality of image lines (201) defining a complete circumferential radar image, each image line corresponding to an azimuthal orientation (204), and wherein each image line (201) contains a plurality of range cells (203), each range cell corresponding to a range to one or more radar antennas (110, 111), and wherein objects (105) detected within azimuthal orientation and range relative to one or more radar antennas (110, 111) are represented by a plurality of hit range cells (203) in the one or more image lines (201), and wherein each range hit cell (203) contains data based on one or more radial velocities of doppler frequency signals and energy of one or more echo signals, whereby for each range hit cell the scan data (501) contains information of range, azimuth orientation, energy of returned radar wave signals and one or more radial velocities of the detected object; and

generating complete data type radar maps (801) based on the obtained scan data, each of the complete data type radar maps (801) containing azimuth, range, radial velocity and received echo energy data of one or more detected objects; it is characterized in that

The processing circuitry (116, 102) is further configured to:

selecting an unconventional type radar map (803) from the full data type radar map (801), the unconventional type radar map (803) having velocity data representing positive and negative radial velocities within the range of observed radial velocities and having at least one predetermined minimum velocity difference between the observed radial velocity having the largest positive value and the observed radial velocity having the largest negative value in absolute value; and is

Generating one or more object trajectories or Unmanned Aerial Vehicle (UAV) trajectories (807), wherein each object/UAV trajectory (807) is based on at least two radar maps (803) of an unconventional type with a match between corresponding data of the at least two radar maps.

21. The FMCW radar system (101) of claim 20, wherein the system (101) is configured to provide scan data indicative of radial velocity over a predetermined positive velocity range and a predetermined negative velocity range, the predetermined negative velocity range being the same size as the predetermined positive velocity range, and

wherein the predetermined minimum speed difference between the observed radial speed having the largest positive value and the observed radial speed having the largest negative value in absolute value is at least 50%, such as at least 60%, such as at least 70% or such as at least 75% of the combined positive and negative speed range.

22. The FMCW radar system (101) of claim 21, wherein the system (101) is configured to provide scan data indicative of relative velocity in a range of-30 m/s to +30m/s, and

wherein the predetermined minimum speed difference between the observed radial speed having the largest positive value and the observed radial speed having the largest negative value in absolute value is at least 45 m/s.

23. The FMCW radar system (101) of any of claims 20 to 22, wherein the matching between the corresponding data includes matching between radial velocity data, matching between range data, and/or matching between echo energy data.

24. The FMCW radar system (101) of any one of claims 20-23, wherein the processing circuitry (116, 102) is configured to:

for the generated map of object/UAV trajectories (803), determining:

an external energy sum, which is the sum of the echo energies (1602, 1603) of the range cells representing positive and negative radial velocity signals within an outer velocity range of the observed radial velocity range;

a central energy sum (1601, 1602, 1603) which is the sum of the echo energies (1601) of range cells representing radial velocity signals in the central range of the observed radial velocity range, and/or a total energy sum (1601, 1602, 1603) which is the sum of the echo energies of range cells representing all radial velocity signals of the observed radial velocity range; and is

Wherein the processing circuitry (116, 102) is further configured to:

classifying the object/UAV trajectory as either a real UAV trajectory (809c) or a non-real UAV trajectory (809d) based at least in part on a comparison of the determined external energy sum (1602, 1603) to the determined central energy sum (1601), and/or to the determined total energy sum (1601, 1602, 1603).

25. The FMCW radar system (101) of claim 24, wherein the processing circuitry (116, 102) is configured to:

classifying the object/UAV trajectory as a non-real UAV trajectory (809d) when the determined external energy sum (1602, 1603) is below a predetermined fraction of the determined central energy sum (1601), such as below 1/1000 of the central energy sum.

26. The FMCW radar system (101) of claim 24, wherein the processing circuitry (116, 102) is configured to:

classifying the object/UAV trajectory as a non-real UAV trajectory when the determined external energy sum is below a predetermined fraction of the determined total energy sum.

27. The FMCW radar system (101) of any one of claims 24-26, wherein the processing circuitry (116, 102) is configured to:

classifying the object/UAV trajectory as either a real UAV trajectory (809c) or a non-real UAV trajectory (809d) based at least in part on a comparison of the determined total energy sum (1601, 1602, 1603) to a predetermined maximum energy representing a predetermined maximum radar scattering cross section.

28. The FMCW radar system (101) of any one of claims 24-27, wherein the processing circuitry (116, 102) is configured to: when the determined total energy sum (1601, 1602, 1603) is higher than a value representing, for example, 1m²Is determined, the object/UAV trajectory is classified as a non-UAV or large UAV trajectory at a predetermined maximum energy of a predetermined maximum radar scattering cross-section, such as a maximum radar scattering cross-section.

29. The FMCW radar system (101) of any one of claims 24-28, wherein the processing circuitry (116, 102) is configured to: classifying the object/UAV trajectory as a real UAV trajectory (809c) when the determined sum of external energies (1602, 1603) is above a predetermined fraction of the determined sum of central energies (1601) and/or above a predetermined fraction of the determined sum of total energies (1601, 1602, 1603), and when the determined sum of total energies (1601, 1602, 1603) is below a predetermined maximum energy representing a predetermined maximum radar scattering cross-section.

30. The FMCW radar system (101) of any one of claims 20 to 29, wherein the processing circuitry (116, 102) is configured to:

a complete data type radar map (801) is generated based on grouping neighboring hit range cells (203) within one or more image lines (201) of a complete circumferential radar image (200) having matching range, azimuth and radial velocity data.

31. A frequency modulated continuous wave, FMCW, radar system (101), the system comprising:

processing circuitry (116, 102) configured to:

providing scan data (500) based on the transmitted and received FMCW radar signals (205, 300) and the azimuthal position of the one or more antennas (110, 111), the scan data (501) representing range cells (203) within image lines (201) of a circumferential radar image (200), wherein each radar image (200) contains a plurality of image lines (201) defining a complete circumferential radar image, each image line corresponding to an azimuthal orientation (204), and wherein each image line (201) contains a plurality of range cells (203), each range cell corresponding to a range to one or more radar antennas (110, 111), and wherein objects (105) detected within azimuthal orientation and range relative to one or more radar antennas (110, 111) are represented by a plurality of hit range cells (203) in the one or more image lines (201), and wherein each range hit cell (203) contains data based on one or more radial velocities of doppler frequency signals and one or more echo signal energies, whereby for each range hit cell the scan data (500) contains information of range, azimuth orientation, energy of returned radar wave signals and one or more radial velocities of the detected object; it is characterized in that

The processing circuitry (116, 102) is further configured to:

generating complete data type radar maps (801) based on the obtained range cell scan data, each said complete data type radar map (801) containing azimuth, range, radial velocity and received echo energy data of one or more detected objects;

selecting an unconventional type radar map (802, 803) from the complete data type radar map (801), the unconventional type radar map (803) having velocity data representing positive and negative radial velocities within the observed range of radial velocities;

generating one or more object trajectories or Unmanned Aerial Vehicle (UAV) trajectories (807), wherein each object/UAV trajectory (807) is based on at least two radar maps (803) of an unconventional type with a match between corresponding data of the at least two radar maps;

for the generated map of object/UAV trajectories (803), determining:

an external energy sum, which is the sum of the echo energies (1602, 1603) of the range cells representing positive and negative radial velocity signals in first and second external velocity ranges outside the central range of the observed radial velocity range;

a central energy sum (1601, 1602, 1603) which is the sum of the echo energies (1601) of range cells representing radial velocity signals within said central range of the observed radial velocity range, and/or a total energy sum (1601, 1602, 1603) which is the sum of the echo energies of range cells representing all radial velocity signals of the observed radial velocity range; and is

Wherein the processing circuitry (116, 102) is further configured to:

32. The FMCW radar system (101) of claim 31, wherein the processing circuitry (116, 102) is configured to:

33. The FMCW radar system (101) of claim 31 or 32, wherein the processing circuitry (116, 102) is configured to: classifying the object/UAV trajectory as a real UAV trajectory (809c) when the determined sum of external energies (1602, 1603) is above a predetermined fraction of the determined sum of central energies (1601) and/or above a predetermined fraction of the determined sum of total energies (1601, 1602, 1603), and when the determined sum of total energies (1601, 1602, 1603) is below a predetermined maximum energy representing a predetermined maximum radar scattering cross-section.

34. The FMCW radar system (101) of any one of claims 31-33, wherein the processing circuit (102) is configured to:

35. The FMCW radar system (101) of any one of claims 31-33, wherein the processing circuitry (116, 102) is configured to:

classifying the object/UAV trajectory as a non-real UAV trajectory (809d) when the determined external energy sum (1602, 1603) is below a predetermined fraction of the determined total energy sum (1601, 1602, 1603).

36. The FMCW radar system (101) of any one of claims 32-35, wherein the processing circuitry (116, 102) is configured to: when the determined total energy sum (1601, 1602, 1603) is higher than a value representing, for example, 1m²Is determined, the object/UAV trajectory is classified as a non-UAV or large UAV trajectory at a predetermined maximum energy of a predetermined maximum radar scattering cross-section, such as a maximum radar scattering cross-section.

37. The FMCW radar system (101) of any one of claims 31-36, wherein the processing circuitry (116, 102) is configured to:

determining a radial velocity boundary of the central velocity range, and

determining a radial speed boundary (1604) of the first outer speed range (1602) comprising a negative radial speed signal and a radial speed boundary (1605) of the second outer speed range (1603) comprising a positive radial speed signal, the determination of the speed boundary (1604) of the first speed range and the speed boundary (1605) of the second speed range being based on the observed radial speed boundaries of the entire speed range and the central speed range (1601).

38. The FMCW radar system (101) of any one of claims 31-37, wherein the processing circuitry (116, 102) is configured to:

a radial velocity boundary (1604, 1605) of the central velocity range (1601) is determined based on a change in an observed energy level of the echo radar signal with radial velocity.

39. The FMCW radar system (101) of claim 38, wherein the processing circuitry (116, 102) is configured to:

determining that the observed energy level decreases to a local minimum on both sides of the center speed of the observed speed range;

determining the radial velocity boundary (1604, 1605) of the central velocity range (1601) as a radial velocity at which the observed energy levels on both sides of the central velocity have increased by a predetermined factor from the observed local minimum.

40. The FMCW radar system (101) of any of claims 31-39, wherein the processing circuitry (116, 102) is configured to:

41. An Unmanned Aerial Vehicle (UAV) system, the system comprising:

a control station for controlling a first cooperative Unmanned Aerial Vehicle (UAV), the control station configured for exchanging telemetry data with the first UAV, including data instructing the first UAV to follow a flight path based on flight plan instructions received from the control station, and the first UAV may be provided with a transponder containing identification Information (ID) of the first UAV, and the first UAV and the control station may be configured for exchanging transponder data;

a processing circuit configured to:

generating and storing one or more UAV object trajectories, wherein each UAV object trajectory is based on at least two unconventional type radar maps having a match between corresponding data of the at least two unconventional type radar maps, and wherein each UAV object trajectory contains object data corresponding to data of the matched maps;

receiving telemetry data and/or transponder data of the first UAV;

determining, for each of the UAV object trajectories, whether there is a match between the data of the UAV object trajectory and corresponding telemetry data and/or transponder data of the first UAV; and

classifying the UAV of the UAV object trajectory as a first cooperating UAV when corresponding data of the UAV object trajectory and received telemetry data and/or transponder data satisfy a predetermined matching condition, and classifying the UAV of the UAV object trajectory as a second non-cooperating UAV when the predetermined matching condition is not satisfied.

42. A system according to claim 41, wherein the radar system comprises a Doppler type radar, such as a frequency modulated continuous wave, FMCW, radar.

43. The system of claim 41 or 42, wherein the processing circuitry is configured to generate UAV object trajectories containing data representing position, radial velocity and size of tracked objects based on data of matched radar maps obtained from the received scan information.

44. The system of any of claims 41 to 43, wherein the first cooperative UAV includes a Global Positioning System (GPS) and the telemetry data forwarded by the control station to the processing circuitry of the first cooperative UAV includes location data based on GPS data.

45. The system of any of claims 41 to 44, wherein the first cooperating UAV contains a transponder with ID data, and the transponder data forwarded by the first UAV to the control station represents ID data and may also represent location data, such as altitude data or altitude data.

46. A system according to any of claims 41 to 45, wherein the predetermined matching condition to be satisfied between the data of the stored trajectory and the received telemetry data and/or transponder data comprises: a match between position data, a match between velocity data and/or a match between size data, and

wherein the processing circuitry is configured to: determining when a matching condition is satisfied for a corresponding set of data based on a predetermined threshold difference between the matched data.

47. The system of any of claims 41 to 46, wherein when the UAV of a UAV object trajectory is classified as a second non-cooperative UAV, the control station is configured to:

generating flight plan instructions based at least in part on object data from object trajectories of the second non-cooperative UAV and forwarding the flight plan instructions to the first cooperative UAV.

48. The system of any of claims 41 to 47, wherein when the UAV of a UAV object trajectory is classified as a second non-cooperative UAV, the control circuitry is configured to:

generating and forwarding action instructions to the first UAV based on information obtained from the object trajectory of a second UAV, and wherein

49. The system of claim 8, wherein the control station is configured to: generating and forwarding flight interference action instructions to the first cooperative UAV based on information obtained from the object trajectory of the second non-cooperative UAV, and wherein the first cooperative UAV is configured to: performing a flight route or flight plan intervention maneuver for the second non-cooperative UAV based on the received flight intervention maneuver instruction.

50. The system of any one of claims 47 to 49, wherein the control station is configured to: generating the flight plan instructions and/or action instructions based on position data and/or dimensional data obtained from the object trajectory of the second non-cooperative UAV.

51. The system of any one of claims 47 to 50, wherein the control station is configured to: generating the flight plan instructions and/or action instructions for the first cooperating UAV based at least in part on the received telemetry data and/or transponder data of the first cooperating UAV.

52. The system of any of claims 41-51, where the first cooperating UAV incorporates a camera and is configured to transmit telemetry data comprising video signals to the control station, and where the control station is configured to generate flight path information based at least in part on the received video signals.

53. The system of claim 50 or 52, wherein the control station is configured to: generating and forwarding flight plan information based on position data obtained from the object trajectory of the second non-cooperating UAV until the second non-cooperating UAV is detected within the received video signal.

54. The system of claim 53, wherein, when the second non-cooperating UAV is detected within the received video signal, the control station is configured to generate and forward flight plan information based on the received video signal.

55. The system of any of claims 41 to 54, wherein the selected unconventional type radar map has velocity data representing positive and negative radial velocities with at least one predetermined minimum velocity difference between an observed radial velocity having a maximum positive value and an observed radial velocity having a negative value with a maximum absolute value.

56. The system of any of claims 41 to 55, where the generated UAV trajectory is based, at least in part, on a radar map with matching radial velocity data; based at least in part on the radar map with the matched range data; and/or based at least in part on a radar map with matched echo energy data.

57. The system of any one of claims 41 to 56, wherein the processing circuitry is configured to match echo energy data of two unconventional types of radar maps by:

determining a sum of echo energies of all radial velocity signals representing each of the two non-conventional type radar maps being matched, and

58. The system of claim 57, wherein the processing circuitry is configured to match echo energy data of two unconventional types of radar maps by:

determining, for each of the two non-conventional type radar maps that are matched, a sum of central echo energies corresponding to a central radial velocity span within the observed range of radial velocities, an